A bidirectional fast charging orderly charging and discharging method and system for a battery swap station

Abstract

The application provides a bidirectional fast charging orderly charging and discharging method and system for a battery swap station, establishes a decision variable of a charging machine charging / discharging state in the battery swap station, constructs a multi-objective function and a constraint condition, forms a multi-objective optimization model, the objective function of the optimization model includes maximizing the battery swap station income, minimizing the root mean square of the power grid load, and minimizing the power grid peak-valley difference, uses the decision variable as a particle, solves the multi-objective optimization model by using a discrete particle swarm algorithm with adaptive inertia weight, and determines the charging / discharging power of each charging machine in the battery swap station in different time periods under the conditions of meeting the battery swap demand and each constraint condition. According to the method and system provided by the application, the charging machine can select fast charging, normal charging, slow charging, and discharging of the charging machine in the battery swap station according to the load condition of the power grid and the battery swap demand of the user in a certain period, and determine the control strategy under the condition of the known multi-objective function and multi-constraint.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of orderly charging and discharging control of battery swap stations, and particularly relates to a two-way fast charging orderly charging and discharging method and system for a battery swap station. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] As a clean new energy transportation tool, electric vehicles are attracting more and more attention. Battery swap stations (BSS) provide an alternative method for charging electric vehicles (EV). In the swap mode, a unified standardized battery is used to supplement the power of electric vehicles, wherein the battery is recyclable, and a complete battery cycle includes a battery replacement process and a battery charging process, as shown in the principle of Figure 1 As the popularity of battery swap technology and the continuous expansion of the swap scale, the charging load in the battery swap station will also increase day by day. If there is no orderly charging scheduling, the superimposition of the charging load and the grid basic load may form a "peak on peak" phenomenon, and the increase of the peak-valley difference will lead to an increase in the operation loss of the power system and cause an impact on the grid load. SUMMARY

[0004] In order to solve the above problems, the present application provides a two-way fast charging orderly charging and discharging method and system for a battery swap station. According to the method and system provided by the present application, the charging machine can select fast charging, normal charging, slow charging, and discharging of the charging machine in the battery swap station according to the load condition of the power grid and the swap demand of the user in a certain period, and determine the control strategy under the condition of multiple objective functions and multiple constraints.

[0005] According to some embodiments, the present application adopts the following technical solutions:

[0006] A two-way fast charging orderly charging and discharging method for a battery swap station, comprising the following steps:

[0007] Establishing a decision variable of the charging / discharging state of the charging machine in the battery swap station;

[0008] Constructing a multi-objective function and a constraint condition to form a multi-objective optimization model, wherein the objective function of the optimization model includes maximizing the swap station revenue, minimizing the root mean square of the grid load, and minimizing the grid peak-valley difference;

[0009] Taking the decision variable as a particle, using a discrete particle swarm algorithm with adaptive inertia weight to solve the multi-objective optimization model, and determining the charging / discharging power of each charging machine in the battery swap station in different time periods under the conditions of meeting the swap demand and each constraint condition.

[0010] As an alternative embodiment, the specific process of establishing the decision variable of the charging / discharging state of the charger in the battery swap station includes: representing the slow charging, normal charging, fast charging and discharging state of each charger in different time periods by different state variables, the charger can only be in a unique state in different time periods, and the state variable is 1, which indicates that the charger is in the state in the time period, and the state variable is 0, which indicates that the charger is not in the state in the time period.

[0011] As an alternative embodiment, the revenue of the battery swap station includes the compensation for peak shaving auxiliary services, the sum of the charges paid by electric vehicle users, and the total purchase cost.

[0012] As an alternative embodiment, the minimum root mean square of the grid load is the root mean square of the difference between the sum of the net charging power of all chargers and the basic load of the regional grid in each period and the average power load of the charger superimposed on the grid load in the battery swap station.

[0013] As an alternative embodiment, the minimum grid peak-valley difference is to minimize the maximum value of the sum of the basic load of the regional grid in each period and the net charging power of all chargers in the battery swap station in the corresponding period.

[0014] As an alternative embodiment, the multi-objective optimization model normalizes each objective function and the corresponding weight coefficient.

[0015] As an alternative embodiment, the constraint conditions include charger power constraints, in-station battery capacity constraints, and battery swap demand constraints.

[0016] The charger power constraint is to maintain the voltage and current within a normal threshold range.

[0017] The in-station battery capacity constraint is to maintain the remaining capacity in the battery within a reasonable range.

[0018] The battery swap demand constraint is that the sum of the storage and replenishment of the battery pack at each moment is not less than the demand at the next moment, and a certain margin is left, and the sum of the storage and replenishment of the battery pack at each stage of the battery swap station cannot exceed the storage limit of the battery swap station.

[0019] As an alternative embodiment, the specific process of solving the multi-objective optimization model by using a discrete particle swarm algorithm with adaptive inertia weight includes:

[0020] (1) Configure parameters of the battery swap station, including battery swap station configuration parameters, time-of-use electricity prices of the grid, and probability distribution of the number of electric vehicles arriving at the station per hour;

[0021] (2) initialize particles and parameters of the algorithm, including iteration number, learning factor, inertia weight, search space dimension, number of initialized population individuals, and convergence accuracy;

[0022] (3) determine whether the iteration termination condition is met, if yes, output the solution vector, otherwise go to step (5);

[0023] (4) output the values of each optimization objective function, including load mean square error, load peak-valley difference, and exchange station revenue;

[0024] (5) start calculating the adaptive weight of the particle according to the current iteration number;

[0025] (6) update the particle velocity, and use the sigmoid function to map the particle velocity to 0-1;

[0026] (7) update the particle position and perform shaping processing on the particle position;

[0027] (8) calculate the fitness value of the particle, and update the local and global optimal solutions;

[0028] (9) randomly generate N particle groups, and together with the original M particles to form M+N particle groups;

[0029] (10) select N suitable particles through the concentration selection mechanism, and immunize M unsuitable particles, and go to step (3).

[0030] As a further defined embodiment, in the solving process, the discrete problem space is mapped to the continuous particle motion space, and the value of the particle in the state space is limited to only two values of 0 or 1, and each bit of the velocity represents the possibility of the bit value of the particle position being 0 or 1.

[0031] A bidirectional fast charging orderly charging and discharging system of a battery swap station, comprising:

[0032] a module for establishing decision variables of charging / discharging states of chargers in the battery swap station;

[0033] a module for constructing multi-objective functions and constraint conditions to form a multi-objective optimization model, wherein the objective functions of the optimization model include maximizing the revenue of the battery swap station, minimizing the root mean square of the grid load, and minimizing the peak-valley difference of the grid;

[0034] a module for solving the multi-objective optimization model by using a discrete particle swarm algorithm with adaptive inertia weight, using the decision variables as particles, to determine the charging / discharging power of each charger in the battery swap station in different time periods under the conditions of meeting the battery swap demand and various constraints.

[0035] As an alternative implementation, the battery swap station is provided with a plurality of charging machines, and each charging machine can charge a battery individually, or several charging machines can be combined to simultaneously fast charge a battery.

[0036] Compared with the prior art, the present application has the following beneficial effects:

[0037] The present application establishes 0-1 type decision variables of the charging state of the charging machine, perfects the model of the scheduling operation of the battery swap station, proposes a control strategy under the condition of a multi-objective function and multiple constraints, optimizes the model solving algorithm, and improves the global search ability and convergence precision of the algorithm.

[0038] In order to make the above objectives, features and advantages of the present application more apparent, clear and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are used for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings, which form a part of the specification, are included to provide a further understanding of the application, and are incorporated herein by reference. The illustrations are shown for the purpose of explaining the present application and are not intended to limit the present application.

[0040] Figure 1 A schematic diagram of the battery circulation process of the electric vehicle in the battery swap station;

[0041] Figure 2 A schematic diagram of the fast charging device of the present embodiment;

[0042] Figure 3 A schematic diagram of the typical trimf function of the present embodiment;

[0043] Figure 4 A schematic diagram of the updating method of the particle position of the present embodiment;

[0044] Figure 5 A flow chart of the solving process of the fast charging model of the battery swap cabinet of the present embodiment. DETAILED DESCRIPTION

[0045] The present application will be further described below in combination with the accompanying drawings and embodiments.

[0046] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0047] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0048] The charging station includes a plurality of charging and discharging modules (or chargers). The present embodiment takes three charging modules as an example for illustration.

[0049] As shown in Figure 2 , when the main power part is working, the three charging modules can be used to charge the corresponding batteries respectively, or two charging modules can be used to charge one group of batteries, or three charging modules can be used to charge one group of batteries to achieve fast charging. The rated charging power of each charging and discharging module is 20Kw, Figure 2 The main power part is shown in the schematic diagram. The implementation of the function of the switch logic control module relies on six 2P DC contactors, Break1, Break2, Break3, Break4, Break5, and Break6. Break1, Break2, and Break3 need to withstand a charging current of 60kW, and Break4, Break5, and Break6 need to withstand a charging current of 20kW.

[0050] ①When Break1 is closed and the other switches are open, charging and discharging module 1 charges battery 1. Similarly, battery 2 and battery 3 are deduced.

[0051] ②When Break1 and Break4 are closed and the other switches are open, charging and discharging modules 1 and 2 simultaneously charge battery 1. Similarly, battery 2 and battery 3 are deduced.

[0052] ③When Break1, Break4, and Break5 are closed and the other switches are open, charging and discharging modules 1, 2, and 3 simultaneously charge battery 1. Similarly, battery 2 and battery 3 are deduced.

[0053] Because the battery replacement station should first ensure that the number of fully charged batteries in each time period should meet the battery replacement demand of that time period, and a certain margin is left to make the battery supply of the next time period not so tense, the number state of the batteries in the station will be an important reference factor for our scheduling control strategy. First, set the upper limit of the storage amount of the fully charged batteries in the battery replacement station as N max , and the lower limit as N min , then the number of fully charged batteries in the battery replacement station at any time of the day can determine the possibility of being in which number state through a specific membership function. We choose the typical trimf function embedded in MATLAB, as shown in Figure 3 .

[0054]

[0055] Considering that we only select N max and N min two values as function parameters, the above function is divided into two half-triangle membership functions.

[0056]

[0057]

[0058] trimfL defines the low-battery-storage lower limit of each period battery number valley characteristics of the low-battery-storage type half-triangle trimfL function, it can be seen that when the battery number N j of the jth time period is closer to the lower limit, trimfL is closer to 1, and vice versa; trimfH defines the high-battery-storage peak characteristics of the high-battery-storage type half-triangle trimfH function, when the battery number N j of the jth time period is closer to the upper limit, trimfH is closer to 1, and vice versa.

[0059] Correspondingly, the battery number state division function is set as:

[0060]

[0061] σ = 0.9, S = 0, indicating that the battery number is insufficient, S = 1, indicating that the battery number is in a normal range, and S = 2, indicating that the battery number is close to the upper limit value.

[0062] The battery swap station is connected to the grid, and the output of the energy storage system of the charger and the battery is optimized and dispatched. Considering the economy of the battery swap station operation, if the battery number is in a normal range or insufficient state during the low load valley of the grid, the battery swap station should try to charge quickly after optimization and dispatch, preferentially meet the demand of the battery swap station during this period, and at the same time realize the "filling valley effect" of the load. The total load is the superposition value of the regional conventional basic load and the orderly charging load. If the battery number is at a relatively high level, the excess power is stored in the energy storage system or sold to the grid according to the time-of-use electricity price. The high price period corresponds to the peak load of the grid, at which time the charging number of the charger in the battery swap station is very small after dispatch. At this time, the energy storage system can discharge to the grid to bear part of the power supply function when the battery number is large and can meet the battery swap demand, so as to achieve the effect of reducing the peak load of the grid. The load is mainly the conventional basic load minus the power supply power of the battery swap station. As for whether to adopt the charging mode or the discharging mode and what power charging mode should be adopted in different states, Table 1 shows the orderly charging and discharging control strategy under the combination of various load states and battery number states.

[0063] Table 1 Orderly charge and discharge control strategy

[0064]

[0065] To mathematically model a battery swap station with fast charging characteristics, first clarify all charging states. Figure 2 The rated power of each charging and discharging module is 20 kW. When only Break 1 is closed, it is a slow charging state with a power of 20 kW. When Break 1 and Break 4 are closed, it represents a normal charging state. When Break 1, Break 4, and Break 5 are closed, it represents a fast charging state with a power of 60 kW. In the discharge state, the EV battery supplies power to the charging and discharging module, and then finally flows to the grid with a power of 20 kW. Therefore, the 0-1 type decision variable is defined as:

[0066] C1 ij The value is 1, indicating that the i-th charger is slow charging in the j-th period, i = 1, 2, ... I, j = 1, 2, ... J

[0067] C2 ij The value is 1, indicating that the i-th charger is charging normally in the j-th period, i = 1, 2, ... I, j = 1, 2, ... J

[0068] C3 ij A value of 1 indicates that the i-th charger is fast charging in the j-th period, i = 1, 2, ... I, j = 1, 2, ... J

[0069] D ij The value is 1, indicating that the i-th charger is discharging in the j-th time period, i = 1, 2, ... I, j = 1, 2, ... J

[0070] Secondly, it must be satisfied that the same charger can only be in one of the states of discharging, charging or static in a certain period of time. Therefore, the above four decision variables meet the internal constraints:

[0071] c1 ij +c2 ij +c3 ij +d ij ≥0

[0072] c1 ij +c2 ij +c3 ij +d ij ≤1

[0073] Objective function 1: maximize the revenue of the battery swap station, where the revenue of the battery swap station includes compensation for peak shaving auxiliary services, charges paid by electric vehicle users, and reduction in total electricity purchase costs before and after optimization. After the orderly management of electric vehicle battery charging, as a third-party independent subject resource, the battery swap station participates in peak shaving auxiliary services and obtains auxiliary service compensation, which is shared by the battery swap station and the electric vehicle user.

[0074]

[0075]

[0076] QC ij = (p1·c1 ij +p2·c2 ij +p3·c3 ij )·ΔT

[0077] QD ij = p d d ij ·ΔT

[0078] δ represents the battery swap service fee, 10 yuan / time n j represents the number of electric vehicles served in the jth time period, χ is the battery swap retail price of the electric vehicle user, 1.5 yuan / kwh, Q rate is the rated capacity of the battery, Q jk is the remaining capacity of the kth electric vehicle arriving in the jth period, and COST represents the total charging capacity cost of all chargers in the battery swap station within a day.

[0079] QC ij represents the charging capacity of the ith charger in the jth time period, η charge is the charging efficiency, β1 represents the electricity selling price of the power grid, QD ij represents the discharging capacity of the ith charger in the jth time period, η discharge represents the discharging efficiency, β2 is the on-grid price of the energy storage system to the power grid, and ΔT represents the length of the divided time period, p1, p2, p3, and p d represent the slow charging power, normal charging power, fast charging power, and discharging power of the charger, respectively.

[0080] Objective function 2: minimize the root mean square of the load, which represents the dispersion of the grid load. If the grid load changes greatly, it means that the grid load fluctuates greatly and the grid loss is large. Conversely, the grid load fluctuation and power loss are small. From the perspective of safe operation of the power grid, it is necessary to appropriately smooth the fluctuation of the grid load and reduce the energy loss of the grid. In order to avoid considering complex topological structures when minimizing the energy loss of the grid, the model is established by minimizing the root mean square of the grid load.

[0081]

[0082]

[0083]

[0084] The net charging power of all chargers in the jth time period of the battery swap station is PS j , J represents the number of time periods in a day, PL j is the basic load of the regional power grid j period, P av is the average daily load before adjustment, P av is the average power load of the battery swap station in a day after superimposing the grid load.

[0085] Objective function 3: taking the peak value of the load curve as the objective function:

[0086] F 13 = min{max(PL j + PS j )}, j = 1, 2, 3…J

[0087] Multi-objective function joint optimization scheduling: In order to unify the measurement and unit of the three objective functions, the three objective functions are normalized and given corresponding weight coefficients.

[0088] F = min{λ1·F 1rated / F 11 + λ2F 12 / F 2rated + λ3F 13 / F 3rated}

[0089] Where F 1rated , F 2rated , F 3rated are the rated values of the three objective functions, respectively.

[0090] Constraint condition 1: charger power constraint, the purpose of power constraint is to maintain the voltage and current within a normal threshold range, avoid high power damage to charging equipment, or low power affect the charging and discharging rate.

[0091] P cmin ≤ p1,p2,p3≤ P cmax

[0092] P cmax = min(P kemax , P kbmax , P klmax )

[0093] P dmin≤P d ≤P dmax

[0094] P cmin 、P cmax Represents the minimum charging load power and maximum charging load power of the battery. kemax P represents the maximum charging power that the charger can provide to the battery to be charged during the j period. kbmax P is the maximum charging power that the battery to be charged in the battery storage of the battery swap station can withstand. klmax P is the power transmission capacity of the power supply line in the battery swap station. dmin 、P dmax They refer to the minimum discharge load power and maximum discharge load power of the battery respectively.

[0095] Constraint 2: In-station battery power constraint. This constraint ensures that the remaining power in the battery remains within a reasonable range. It neither allows overcharging, which shortens the battery life, nor allows the remaining power to be too low, which affects the travel of electric vehicle users.

[0096] Q min ≤Q jk ≤Q max ,j=1,2,…J,k=1,2,…K

[0097] Q jk =Q j-1k +{(p1·c1 ij +p2·c2 ij +p3·c3 ij )·η charge -p d d ij / η discharge}·ΔT

[0098] Q jk Q is the power of the kth battery replaced in the battery swap station in the jth period, min Q represents the lower limit of the charging capacity of the battery to be charged in period j to meet the demand for battery replacement, max Represents the upper limit of charging power required for the battery to be charged during period j to meet the battery replacement demand.

[0099] Constraint 3: Battery swap demand constraint. The sum of the battery pack storage capacity and replenishment capacity at each stage of the battery swap station must be able to meet the user's battery swap demand. That is, the sum at each moment must not be less than the demand at the next moment, and a certain margin must be left. At the same time, the sum of the battery pack storage capacity and replenishment capacity at each stage of the charging and swap station cannot exceed the storage limit M of the charging and swap station. The initial value of the fully charged battery storage capacity per day defaults to M.

[0100] (1+μ)·M j ≤S jMj= 1,2,..., J

[0101]

[0102] S j Mj represents the reserve quantity in the battery storage of the battery swap station at the jth period, M j Mj represents the swap demand of the battery swap station at the jth period. M represents the total amount of reserve batteries at the initial time of the day, and the default is the maximum number of batteries that the battery swap station can store, M. j Mj represents the number of fully charged batteries at the jth period, and μ represents the margin. This constraint is reflected in the SOC of the battery, which means that the reserve capacity of the battery storage of the battery swap station at the jth period must be greater than the demand of the users who swap batteries at that period.

[0103] In summary, the optimization scheduling of the battery swap station is to set the charging (discharging) power of each charger in the battery swap station at the 96 time periods in a day, so as to maximize the comprehensive benefits of the objective function, that is, to minimize the load mean square error and the peak-valley difference of the power grid and maximize the revenue of the battery swap station, while meeting the swap demand of the electric vehicle users and the constraints mentioned above. This is a multi-objective nonlinear constrained optimization problem, and the classical intelligent algorithm has high efficiency, fast convergence speed, and is more likely to find the global optimal solution.

[0104] Particle swarm optimization algorithm is a kind of intelligent evolutionary algorithm, which starts from a random solution and finds the optimal solution through iteration. The algorithm evaluates the quality of the solution by fitness and finds the global optimum by following the current optimal value. Each particle in the group has its own position vector and velocity vector, and most importantly, it also has a fitness value, which is determined by the objective function. In this case, since the decision variables are 0-1 type variables, a discrete particle swarm optimization algorithm is used. The updating method of the position and velocity of the particle (decision variable) in the iteration process is as follows:

[0105] v i,j (k+1) = ωv i,j (k) + c1r1[p i,j (k) - x i,j (k)] + c2r2[p g,j (k) - x i,j (k)]

[0106] x i,j (k+1) = x i,j (k) + av i,j (k+1), j = 1, 2,..., n

[0107]

[0108] The above formulas are all solution formulas for particle swarm optimization under continuous variables. k represents the number of iterations, where ω is a non-negative number called the inertia factor, which adjusts the search range of the solution space. c1 and c2 are learning factors. c1 is a constant based on the individual's own experience, c2 is based on the group's experience, and r1 and r2 are random numbers that change in the range of [0 1]. i,j (k) is the optimal solution of the particle individual history, p g,j (k) is the optimal solution of the particle swarm, and a is the constraint factor, the purpose of which is to control the weight of the speed, and its value is affected by the learning factor. In the discrete particle swarm algorithm, we can map the discrete problem space to the continuous particle motion space and make appropriate modifications, while still retaining the speed-position update strategy of the continuous variable particle swarm algorithm. The values ​​of particles in the state space are limited to 0 and 1, and each bit of the speed represents the possibility that the bit corresponding to the particle position can take the value 0 / 1. Therefore, in the discrete particle swarm algorithm, the update formula of the particle speed remains unchanged, but the value of each bit of the individual optimal position and the global optimal position can only be 0 or 1. The particle position is updated as follows:

[0109]

[0110]

[0111] Here, the sigmoid function is used to map the particle velocity to a range between 0 and 1. Since the performance of the traditional particle swarm algorithm is largely affected by the inertia weight parameter ω, in order to overcome the shortcomings of fixed parameters, we use an adaptive inertia weight factor:

[0112] ω=ω max -(ω max -ω min )·k / MaxDT

[0113] At the beginning of the iteration, the value of k is small and ω is close to the maximum value ω max , which helps to jump out of the local optimal solution. When the iteration is about to terminate, the value of k is close to the iteration upper limit MaxDT, and ω moves closer to the minimum value, which increases the convergence speed of the algorithm.

[0114] The discrete particle swarm optimization algorithm solution steps are as follows:

[0115] ① Input various parameters of the battery swap station, including the configuration parameters of the battery swap station, the time-of-use electricity price of the power grid, and the probability distribution of the number of electric vehicles arriving at the station per hour;

[0116] ② Start initializing particles and various algorithm parameters, including the number of iterations, learning factor, inertia weight, search space dimension, number of individuals in the initialization group, and convergence accuracy;

[0117] ③Judge whether the iteration termination condition is met, if yes, output the solution vector, otherwise, go to step ⑤;

[0118] ④Output each optimization objective function value, including load mean square error, load peak-valley difference and BSS benefit;

[0119] ⑤Start to calculate the adaptive weight of the particle according to the current iteration number;

[0120] ⑥Update the velocity of the particle, and map the velocity of the particle to 0-1 by using the sigmoid function;

[0121] ⑦Update the position of the particle, and perform the shaping processing on the position of the particle;

[0122] ⑧Calculate the fitness value of the particle, and update the local and global optimal solution;

[0123] ⑨Randomly generate N particle groups, and combine the original M particles to form M+N particle groups;

[0124] ⑩Select N suitable particles by the concentration selection mechanism, and immunize M unsuitable particles, and then go to step ③.

[0125] The above describes the specific embodiments of the present application in combination with the drawings, but is not a limitation on the protection scope of the present application, and those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A bidirectional fast charging orderly charging and discharging method for a battery swap station, characterized in that, The method comprises the following steps: establishing a decision variable of a charging / discharging state of a charger in the battery swap station, and the specific process comprises: representing a slow charging, normal charging, fast charging and discharging state of each charger in different time periods by different state variables, and each charger can only be in a unique state in different time periods, and the state variable is 1, which indicates that the charger is in the state in the time period, and the state variable is 0, which indicates that the charger is not in the state in the time period; a multi-objective function and a constraint condition are constructed to form a multi-objective optimization model, and the objective function of the optimization model comprises maximizing the battery swap station revenue, minimizing the root mean square of the power grid load and minimizing the power grid peak-valley difference; wherein the constraint condition further comprises a battery swap demand constraint, and the battery swap demand constraint is that the sum of the storage amount and the replenishment amount of the battery pack at each moment is not less than the demand amount at the next moment, and a certain margin is left, and the sum of the storage amount and the replenishment amount of the battery pack of the battery swap station at each stage cannot exceed the storage limit of the battery swap station; the multi-objective optimization model is solved by using a discrete particle swarm algorithm with adaptive inertia weight, and the charging / discharging power of each charger in the battery swap station in different time periods is determined under the conditions of meeting the battery swap demand and the constraint conditions; the process of solving the multi-objective optimization model by using the discrete particle swarm algorithm with adaptive inertia weight comprises: mapping the speed of the particle to 0-1 by using a sigmoid function; and performing shaping processing on the position of the particle; calculating the fitness value of the particle, and updating the local and global optimal solutions; randomly generating N particle populations, and combining the M original particles to form M+N particle populations; selecting N suitable particles by a concentration selection mechanism, and immunizing M unsuitable particles.

2. The orderly charging and discharging method of the bidirectional fast charging of the battery swap station according to claim 1, characterized in that, The battery swap station revenue comprises compensation for peak shaving auxiliary services, the sum of charges paid by electric vehicle users, and the total power purchase cost.

3. The method of claim 1, wherein the method further comprises: determining whether the battery is a new battery or a used battery; and determining whether the battery is a new battery or a used battery based on the battery voltage and the battery current. The minimum root mean square of the power grid load is the difference between the sum of the net charging power of all chargers and the average power load of the power grid after the chargers in the battery swap station are superimposed on the power grid load.

4. The method of claim 1, wherein the method further comprises: determining whether the battery is a new battery or a used battery; and determining whether the battery is a new battery or a used battery based on the battery voltage and the battery current. The minimum power grid peak-valley difference is the minimum of the maximum value of the sum of the base load of the regional power grid and the net charging power of all chargers in the battery swap station in the corresponding time period.

5. The method of claim 1, wherein the method further comprises: determining whether the battery is a new battery or a used battery; and determining whether the battery is a new battery or a used battery based on the battery voltage and the battery current. The multi-objective optimization model normalizes each objective function and assigns a corresponding weight coefficient.

6. The method of claim 1, wherein the method further comprises: determining whether the battery is a new battery or a used battery; and determining whether the battery is a new battery or a used battery based on the battery voltage and the battery current. The constraint condition comprises a charger power constraint and a battery capacity constraint in the station; The charger power constraint is that the voltage and current are maintained within a normal threshold range; The battery capacity constraint in the station is that the remaining capacity in the battery is maintained within a reasonable range.

7. The method of claim 1, wherein the method further comprises: determining whether the battery is a new battery or a used battery; and determining whether the battery is a new battery or a used battery based on the battery voltage and the battery current. The specific process of solving the multi-objective optimization model by using the discrete particle swarm algorithm with adaptive inertia weight comprises: (1) configuring parameters of the battery swap station, including battery swap station configuration parameters, time-of-use electricity prices of the power grid, and probability distribution of the number of electric vehicles arriving at the station per hour; (2) initializing particles and parameters of the algorithm, including the number of iterations, a learning factor, an inertia weight, the dimension of the search space, the number of individual groups in the initialization population, and the convergence accuracy; (3) judging whether the iteration termination condition is met, if yes, outputting the solution vector, otherwise, turning to step (5); (4) outputting each optimization objective function value, including load mean square error, load peak-valley difference and exchange station revenue; (5) starting to calculate the adaptive weight of the particle according to the current iteration number; (6) updating the velocity of the particle, and mapping the velocity of the particle to 0-1 using a sigmoid function; (7) updating the position of the particle, and performing shaping processing on the position of the particle; (8) calculating the fitness value of the particle, and updating the local and global optimal solutions; (9) randomly generating N particle groups, and combining the M particles with the N particle groups to form M+N particle groups; (10) selecting N suitable particles through a concentration selection mechanism, and immunizing M unsuitable particles, and turning to step (3).

8. The bidirectional fast charging orderly charging and discharging method of a battery swap station according to claim 1 or 7, characterized in that, In the solving process, the discrete problem space is mapped to the continuous particle motion space, and the value of the particle in the state space is limited to only two values of 0 or 1, and each bit of the velocity represents the possibility that the position of the particle corresponds to the bit value of 0 or 1.

9. A bidirectional fast charging orderly charging and discharging system of a battery swap station, characterized in that, It comprises: a module for establishing decision variables of charging / discharging states of chargers in the battery swap station, and the specific process comprises: representing slow charging, normal charging, fast charging and discharging states of each charger in different time periods by different state variables, the charger can only be in a unique state in different time periods, and the state variable is 1, which means that the charger is in the state in the time period, and the state variable is 0, which means that the charger is not in the state in the time period; a module for constructing a multi-objective function and a constraint condition to form a multi-objective optimization model, the objective function of the optimization model comprises a module for maximizing the revenue of the battery swap station, minimizing the root mean square of the power grid load and minimizing the peak-valley difference of the power grid; wherein the constraint condition further comprises a battery swap demand constraint, the battery swap demand constraint is that the sum of the storage amount and the replenishment amount of the battery pack at each moment is not less than the demand amount at the next moment, and a certain margin is left, and the sum of the storage amount and the replenishment amount of the battery pack of the battery swap station at each stage cannot exceed the storage limit of the battery swap station; a module for solving the multi-objective optimization model by using the adaptive inertia weight discrete particle swarm algorithm with the decision variables as particles, to determine the charging / discharging power of each charger in the battery swap station in different time periods under the conditions of meeting the battery swap demand and each constraint condition; The process of solving the multi-objective optimization model by using the adaptive inertia weight discrete particle swarm algorithm comprises: mapping the velocity of the particle to 0-1 using a sigmoid function; and performing shaping processing on the position of the particle; calculating the fitness value of the particle, and updating the local and global optimal solutions; randomly generating N particle groups, and combining the M particles with the N particle groups to form M+N particle groups; selecting N suitable particles through a concentration selection mechanism, and immunizing M unsuitable particles.

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