A method for optimizing operation of a microgrid containing a battery swap station

Abstract

The present invention provides a method for optimizing the operation of a microgrid containing a battery swap station, the method comprising: based on the travel chain of electric vehicles, analyzing the behavior sequence and charging and swapping demand of vehicles using the charging and swapping station, and establishing a charging and swapping demand model; establishing a battery charging rule matrix and a vehicle battery swapping demand matrix based on the behavior data set, and making vehicle and battery matching decisions; establishing an economically optimal upper-level model for the battery swap station; and considering that the load fluctuation of the battery swap station operation will have an impact on the microgrid where it is located, establishing a lower-level model with the minimum load fluctuation variance as the goal. The present invention can ensure the economic efficiency of the battery swap station operation while reducing the carbon emissions of the microgrid system where it is located.

Description

Technical Field

[0001] The present invention relates to the technical field of battery and vehicle optimization matching, and in particular to a method for optimizing operation of a microgrid including a battery swap station. Background Art

[0002] At present, the interaction between electric vehicles and the power grid is mainly based on V2G (vehicle to grid). As the name suggests, V2G refers to electric vehicles connected to the power grid as a distributed load and power source, which can release the electric energy stored in its power battery to the power grid, thereby providing support for optimizing the operation and safety of the power grid. In recent years, the academic community has conducted a lot of research on the interaction between electric vehicles and the power grid under the V2G mode. Many inventions regard electric vehicles as a whole as a mobile distributed energy storage unit, and explain them from the perspectives of load balancing, harmonic pollution, power grid frequency regulation, and vehicle-grid communication.

[0003] At present, there is also research on the B2G (battery to grid) model, which refers to replacing the whole vehicle charging method with power battery charging and swapping. When the battery is low, the owner only needs to replace the power battery to continue his journey without having to wait at the charging location. The replaced power battery is centrally charged at a large charging and swapping station. The whole process can be precisely controlled. The power consumption plan of the large charging and swapping station can be adjusted in an orderly manner according to the actual operation of the power grid to ensure the safe and economical operation of the power grid. The focus of B2G is on the decoupling between electric vehicles and their power batteries.

[0004] However, electric vehicles have fixed departure schedules and routes, and their operation is highly regular. However, due to the uncertainty of factors such as weather, road conditions, driver's driving habits, and carrying capacity, the actual driving time and power consumption of the vehicle deviate from the operation under ideal conditions in the past. If the order of battery replacement and charging of vehicles in the battery replacement station are allowed to proceed in disorder, it will cause certain economic losses to the battery replacement station operating company and the electric vehicle company, simply because there is no unified matching strategy for the planning of battery charging and vehicle battery replacement rules.

[0005] In current research, the state of charge of the battery to be charged is usually low, and the parameters are normally distributed. In fact, it cannot provide enough power, and may even cause over-discharge of the battery, shortening the battery life. The battery that is being charged is selected to provide B2G service, but in practice, the frequent charging and discharging of the battery makes it difficult to predict the time it takes to fully charge, which may cause insufficient number of fully charged batteries in the battery swap station, thus affecting service reliability and increasing the difficulty of formulating charging plans. In addition, existing research has insufficient planning for the operating power of the battery swap station, and there is a large fluctuation in the peak-to-valley difference, which has a greater impact on the power grid, easily causing line damage and voltage mutations, and posing safety hazards. Summary of the invention

[0006] In view of the above-mentioned defects existing in the existing image privacy protection technology, the purpose of the present invention is to provide a method for optimizing the operation of a microgrid containing a battery swap station, which can ensure the economic efficiency of the operation of the battery swap station while reducing the carbon emissions of the microgrid system in which it is located, and achieve double benefits of cost and environment.

[0007] To solve the above problems, the technical solution of the present invention is:

[0008] A method for optimizing operation of a microgrid including a battery swap station, the method comprising the following steps:

[0009] Based on the travel chain of electric vehicles, the behavior sequence and charging and swapping demand of vehicles using battery swap stations are analyzed, and a charging and swapping demand model is established;

[0010] Establish a battery charging rule matrix and a vehicle battery replacement demand matrix based on the behavior data set to make vehicle and battery matching decisions;

[0011] Establishing the economically optimal upper-level model of battery swap stations; and

[0012] Considering that the load fluctuation of the battery swap station operation will have an impact on the microgrid, a lower-level model is established with the goal of minimizing the load fluctuation variance.

[0013] Optionally, the formula for establishing the electric vehicle travel chain model is:

[0014] Among them, TC t is the travel chain of electric vehicle t; is the departure location of electric vehicle t in the i-th travel chain; is the departure time of electric vehicle t in the i-th trip chain; ΔM t,i is the mileage of electric vehicle t in the i-th travel chain; ΔT t,i is the driving time of electric vehicle t in the i-th travel chain; is the destination of electric vehicle t in the i-th travel chain; is the arrival time of electric vehicle t in the i-th travel chain; is the parking time of electric vehicle t after the end of the i-th travel chain.

[0015] Optionally, the SOC formula of the electric vehicle is:

[0016] Among them: SOC r,t is the SOC (%) of the electric vehicle r when it returns to the battery swap station at time t; SOC r,0 is the initial SOC value (%) given to the electric vehicle r at the beginning of the simulation process; S ris the nominal mileage of electric vehicle r; D r,t is the driving distance of electric vehicle r in the tth period; ΔSOC r,t is the SOC increment of electric vehicle r at time t. If it is not charged or replaced during the tth period, it is 0; η r,t is the battery capacity attenuation coefficient of electric vehicle r at time t, which can be simplified to 0.

[0017] Optionally, the SOC of the electric vehicle r when it returns to the charging point at time t can be obtained according to the SOC state conversion function, and combined with the expected travel distance in the next time period, the criterion for the battery pack and vehicle charging and swapping needs can be defined as:

[0018] Among them, D r,t+1 is the expected travel distance of electric vehicle r at t+1; l r,t+1 The mileage margin reserved for electric vehicle r when considering whether to charge or replace the battery.

[0019] Optionally, the process of the battery replacement requirement is:

[0020] Initialize the given electric vehicle departure sequence table, driving energy consumption, and starting SOC information;

[0021] Simulate a round of departure, and order the vehicles and equipment batteries from 1 to n et The remaining backup batteries are numbered in order from n et+1 to n bp serial number;

[0022] Simulate the travel chain according to the departure schedule, and calculate the SOC of the battery carried by each electric vehicle when it returns to the station based on the driving distance and road conditions;

[0023] Determine in sequence whether the battery pack needs to be charged and whether the vehicle needs to be charged. If the battery pack SOC meets the charging requirements, remove the battery pack and add it to the end of the spare battery pack sequence to queue for charging. At the same time, record the current SOC information of the battery pack.

[0024] Load the fully charged batteries at the front of the spare battery group sequence into the electric vehicle, where the spare battery group sequence is arranged in ascending order of subscripts, and the one with the smallest subscript is at the front and is charged first, and then the vehicle is added to the departure sequence and waits for the next round of departure;

[0025] By counting the SOC and time information of the replaced battery pack, combining it with the scheduling of the number and power of charging batteries and the matching strategy for subsequent charging and swapping needs, we can obtain the daily power change curve of the charging and swapping station.

[0026] Optionally, the steps of establishing a battery charging rule matrix and a vehicle battery replacement demand matrix based on the behavioral data set and making a matching decision specifically include: establishing charging demand and battery replacement demand matching indicators, matching the two using a superior and inferior solution distance method and a sorting method to obtain a charging and battery replacement matching solution, and selecting the optimal battery pack and the corresponding numbered vehicle for charging and battery replacement operations.

[0027] Optionally, the formula of the upper model is: in: The cost of purchasing electricity, is the configuration cost of battery energy storage, To participate in the sale of electricity revenue for ancillary services, Carbon market revenue.

[0028] Optionally, the formula of the lower model is: Where: P equ,t It is the real-time equivalent load power of the power station during the dispatching period.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. Aiming at the problem of low efficiency of battery swapping and battery charging caused by mismatch between battery swapping and battery charging operations at battery swapping stations, which affects economic benefits, a method for optimal matching of charging and swapping needs based on ranking and evaluation of superior and inferior solutions is proposed;

[0031] 2. Establish an economic optimal model for the operation of battery swap stations, taking charging and discharging power as the decision-making target and vehicle operation as the constraint, and compare it with scenarios based on demand response and disorderly charging from the perspective of power fluctuation and peak-valley transfer, to develop the optimal allocation plan for charging and discharging power of battery swap stations;

[0032] 3. Aiming at the problems of low economic benefits and large power peak-valley difference in the operation of battery swap stations, a strategy model for battery swap stations to participate in the third-party peak load auxiliary service market based on the B2G model is proposed, and the optimal scheduling strategy of the battery swap station is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0034] Figure 1 A schematic diagram of the structure of a microgrid system including a battery swap station provided in an embodiment of the present invention;

[0035] Figure 2 A flowchart of a method for optimizing operation of a microgrid including a battery swap station provided in an embodiment of the present invention;

[0036] Figure 3A flow chart of a method for agglomerating SOC distribution information of an electric vehicle cluster through sliding simulation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0038] The present invention proposes a method for optimizing the operation of a microgrid including a battery swap station. Figure 1 The system structure of the microgrid containing the battery swap station shown in the figure can meet the operating rules of the vehicles inside the battery swap station, realize the optimal match between battery pack charging and vehicle battery swapping, achieve the economic goal of optimal cost for the battery swap station itself, and reasonably dispatch various energy factors for the microgrid where it is located, so as to realize the reasonable time allocation of the power of the battery swap station.

[0039] Specifically, Figure 2 The flowchart of the method for optimizing the operation of a microgrid containing a battery swap station provided in an embodiment of the present invention is as follows: 1. Battery packs and electric vehicles are only charged and swapped at the battery swap station in the travel chain; 2. All batteries in the battery swap station are of the same specification, and the actual performance differences of each battery are not considered; 3. The battery is installed in the charging cabinet and fully charged under the conditions of meeting the B2G and carbon quota and peak-shaving auxiliary service requirements; 4. The power battery pack is charged at a uniform speed and constant power; 5. The power consumption of electric vehicles is proportional to the driving time, and there are sufficient charging cabinets and chargers in the battery swap station; 6. The driving speed of the electric vehicle remains unchanged during the operation, and after arriving at the battery swap station, if the battery is swapped, the stay time is the battery swap time, that is, the unloading time, and the power battery pack carried by it enters the charging process immediately after being replaced; 7. The internal power demand and power interface capacity of each module of the microgrid are fixed, and the specific power flow and charging and discharging control problems within the nanogrid are not considered; Based on the above assumptions, the method for optimizing the operation of a microgrid containing a battery swap station of the present invention includes the following steps:

[0040] S1: Based on the travel chain of electric vehicles, analyze the behavior sequence of vehicles using charging and swapping stations and the charging and swapping demand, and establish a charging and swapping demand model;

[0041] Specifically, referring to the relevant data of traditional fuel vehicles, an electric vehicle travel chain is constructed, and the Latin hypercube sampling method is used to perform sliding simulation on the driving energy consumption and charging and swapping energy replenishment process of each vehicle in the cluster. According to the stage operation of the vehicle arriving at the station and the battery charging and swapping transfer process in the charging and swapping process, the battery charge state information is condensed.

[0042] The electric vehicle travel chain model is established as shown in the following formula (1):

[0043]

[0044] Among them, TC t is the travel chain of electric vehicle t; is the departure location of electric vehicle t in the i-th travel chain; is the departure time of electric vehicle t in the i-th trip chain; ΔM t,i is the mileage of electric vehicle t in the i-th travel chain; ΔT t,i is the driving time of electric vehicle t in the i-th travel chain; is the destination of electric vehicle t in the i-th travel chain; is the arrival time of electric vehicle t in the i-th travel chain; is the parking time of electric vehicle t after the end of the i-th travel chain.

[0045] Specifically, the destination of the previous travel chain is the departure location of the next travel chain; the mileage of the i-th travel chain of the electric vehicle is a function of the departure time, location and arrival location; the driving time of the i-th travel chain of the electric vehicle is a function of the departure time, location and arrival location; the arrival time of the i-th travel chain of the electric vehicle t is the i-th departure time plus the driving time of the i-th travel chain; the departure time of the i+1-th travel chain of the electric vehicle t is the arrival time of the i-th travel chain plus the parking time after the end of the i-th travel chain, and the formulas are shown in the following formula (2):

[0046]

[0047] in, The mileage function used by electric vehicle t to complete the i-th behavior process; It is the driving time function used by electric vehicle t to complete the i-th behavior process.

[0048] Based on the simplified electric vehicle travel chain model, a charging and swapping demand model is established with three key indicators: the starting time of the return process, the driving distance of the return process, and the arrival time of the return process. The Latin hypercube sampling method, which is more suitable for small sample sizes, is used to screen and sort the samples.

[0049] The SOC change of the electric vehicle is a cumulative process of its driving power consumption and parking charging and battery replacement cycles. That is, the SOC of the electric vehicle is jointly determined by its travel chain in a long operating cycle (for example, one day or one week), which can be expressed as shown in the following formula (3):

[0050]

[0051] Among them, SOC r,t is the SOC (%) of the electric vehicle r when it returns to the battery swap station at time t; SOC r,0 is the initial SOC value (%) given to the electric vehicle r at the beginning of the simulation process; S r is the nominal mileage of electric vehicle r; D r,t is the driving distance of electric vehicle r in the tth period; ΔSOC r,t is the SOC increment of electric vehicle r at time t. If it is not charged or replaced during the tth period, it is 0; η r,t is the battery capacity attenuation coefficient of electric vehicle r at time t, which can be simplified to 0.

[0052] According to the SOC state conversion function, the SOC of the electric vehicle r when it returns to the charging point at time t can be obtained. Combined with the expected travel distance in the next time period, the criterion for the battery pack and vehicle charging and swapping requirements can be defined as shown in formula (4):

[0053]

[0054] Among them, D r,t+1 is the expected travel distance of electric vehicle r at t+1; l r,t+1 The mileage margin reserved for electric vehicle r when considering whether to charge or replace the battery.

[0055] After clearly defining the above relationship, based on the established electric vehicle travel chain, the method of agglomerating the SOC distribution information of the electric vehicle cluster through sliding simulation is as follows: Figure 3 As shown. From a given initial charge value (SOC r,0 ), a sliding cycle simulation is performed on the energy consumption of each individual vehicle in the electric vehicle cluster, as well as the charging and swapping process. To simplify the analysis, it is assumed that the battery swap mode or charging is full (until ).

[0056] During the sliding simulation process, the gradually obvious regular characteristics of electric vehicle clusters can be captured and saved, so as to obtain an SOC distribution that is more in line with the characteristics of the electric vehicle group under study. For example, the SOC distribution of the simulated electric vehicle cluster at each moment is saved in units of days, and at the end of each day, the expected, variance, and variance coefficient distribution of the SOC at each moment are used to compare the regular characteristics of the SOC distribution of the electric vehicle cluster at the corresponding moment between any two days in the last k days. If the SOC distribution differences between days are controlled within a certain threshold range for several consecutive k days, it is considered that the SOC distribution information of this sample electric vehicle group has stabilized. At this point, from the perspective of the electric vehicle cluster, it can be considered that the current SOC agglomeration distribution meets the relevant characteristics of the electric vehicle cluster under study, so it can be used for charging and swapping demand modeling, which can improve the rationality and accuracy of the results.

[0057] Define the total number of power batteries in the station as n bp The total number of electric vehicles is n et vehicles, s is the number of standby batteries (SBP) obtained by the difference between the two, and the process of battery replacement demand is as follows:

[0058] Initialize the given electric vehicle departure sequence table, driving energy consumption, starting SOC and other information;

[0059] Simulate a round of departure, and order the vehicles and equipment batteries from 1 to n et The remaining backup batteries are numbered in order from n et+1 to n bp serial number;

[0060] Simulate the travel chain according to the departure schedule, and calculate the SOC of the battery carried by each electric vehicle when it returns to the station based on the driving distance and road conditions;

[0061] Determine in sequence whether the battery pack needs to be charged and whether the vehicle needs to be charged. If the battery pack SOC meets the charging requirements, remove the battery pack and add it to the end of the spare battery pack sequence to queue for charging. At the same time, record the current SOC information of the battery pack.

[0062] Load the fully charged batteries at the front of the spare battery group sequence into the electric vehicle, wherein the spare battery group sequence is arranged in ascending order of the subscript, and the one with the smallest subscript is at the front and is charged first, and then the vehicle is added to the departure sequence and waits for the next round of departure;

[0063] By counting the SOC and time information of the replaced battery pack, combining it with the scheduling of the number and power of charging batteries and the matching strategy for subsequent charging and swapping needs, we can obtain the daily power change curve of the charging and swapping station.

[0064] S2: Establish a battery charging rule matrix and a vehicle battery replacement demand matrix based on the behavior data set to make matching decisions between vehicles and batteries;

[0065] Specifically, a matching index between charging demand and battery swapping demand is established, and the two are matched using the superior-inferior solution distance ranking method to obtain a charging and battery swapping matching plan, and the optimal battery pack is selected to perform charging and battery swapping operations with the corresponding numbered vehicles.

[0066] Different from the one-to-one correspondence between vehicles and battery packs in the electric vehicle charging mode, in the battery swapping mode, the vehicle and battery pack are separated, and their corresponding relationship affects the charging scheduling space of each battery pack, and further affects the safety and economy of the charging scheme in the station. Therefore, this section proposes a matching model between vehicle battery swapping demand and battery pack charging demand based on the vehicle battery swapping demand and battery pack charging demand model of the charging and swapping station: for different vehicle battery swapping demands, the optimal battery pack charging demand is selected from the charging demands of each battery pack to match. The matching model between vehicle battery swapping demand and battery pack charging demand mainly includes two parts: (1) establishment of matching indicators; (2) proposing a matching strategy based on the entropy weight-TOPSIS method, and obtaining the optimal rechargeable battery and vehicle battery swapping matching according to the above rules.

[0067] S3: Establish the economic optimal upper model of battery swap stations;

[0068] Specifically, an economically optimal upper-level model of the battery swap station is established, taking into account the electricity purchase cost, battery loss cost, battery configuration cost, carbon quota income, and income from participating in peak-shaving auxiliary services. Vehicle operation restrictions and power balance of other output units in the microgrid are used as constraints to obtain the power distribution curve of the battery swap station.

[0069] F1 is the daily operating cost of the team.

[0070]

[0071]

[0072]

[0073]

[0074] in: The cost of purchasing electricity, is the configuration cost of battery energy storage, To participate in the sale of electricity revenue for ancillary services, is the carbon market income, f p,time is the time-of-use electricity price function, N y is the number of days in a year, N c For battery life, C in is the battery purchase cost, S Vis the residual value of the battery, and M is the total number of vehicles operated by the company on that day.

[0075] Introduce the B2G model and use battery swapping as energy storage to participate in the peak-valley dispatching of the power grid. Use battery swapping stations as third-party entities to participate in the battery swapping peak-shaving auxiliary service market and participate in incentive-based demand response.

[0076] S4: Considering that the load fluctuation of the battery swap station operation will have an impact on the microgrid, a lower-level model is established with the goal of minimizing the load fluctuation variance.

[0077] Specifically, F2 is the lower target load fluctuation function, which is measured by the variance of the real-time load and the average load within the scheduling period T.

[0078]

[0079]

[0080] P equ,t It is the real-time equivalent load power of the power station during the dispatching period.

[0081] In order to ensure that the operating cost after optimization of the lower layer is consistent with the optimization result of the upper layer, additional constraints are added on the basis of the upper layer constraints: the charge and discharge amount in each time period is consistent, and the battery loss in each time period is consistent.

[0082] The peak and valley changes of the upper and lower power curves are compared. At the same time, the operating costs of the battery swap stations, battery loss costs, carbon quota benefits, peak-shaving auxiliary service market benefits, wind energy consumption, load variance, and carbon emissions before and after the optimization of the upper and lower layers are compared to verify the economy and low-carbon nature of the model in this section.

[0083] A two-layer optimization model is established. Since the solution after the upper optimization is not smooth and stable, and is affected by time and the electricity consumption habits of the electricity buyer, i.e. the fleet operating company, the load of the charging and swapping station fluctuates with the peak, valley and flat periods, which may cause large fluctuations in the grid load. Therefore, the lower optimization model is considered to be introduced. The decision variable optimized by the lower model is the charging and discharging power. On the basis of the upper optimization, the goal is to minimize the grid load fluctuation caused by the power station operation, and constraints such as load variance and power limit are added to further constrain the charging and discharging power of the battery of the swapping station.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] 1. Aiming at the problem of low efficiency of battery swapping and battery charging caused by mismatch between battery swapping and battery charging operations at battery swapping stations, which affects economic benefits, a method for optimal matching of charging and swapping needs based on ranking and evaluation of superior and inferior solutions is proposed;

[0086] 2. Establish an economic optimal model for the operation of battery swap stations, taking charging and discharging power as the decision-making target and vehicle operation as the constraint, and compare it with scenarios based on demand response and disorderly charging from the perspective of power fluctuation and peak-valley transfer, to develop the optimal allocation plan for charging and discharging power of battery swap stations;

[0087] 3. Aiming at the problems of low economic benefits and large power peak-valley difference in the operation of battery swap stations, a strategy model for battery swap stations to participate in the third-party peak load auxiliary service market based on the B2G model is proposed, and the optimal scheduling strategy of the battery swap station is obtained.

[0088] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for optimizing the operation of a microgrid containing a battery swap station, characterized in that: The method comprises the following steps: Based on the travel chain of electric vehicles, the behavior sequence and charging and swapping demand of vehicles using charging and swapping stations are analyzed, and a charging and swapping demand model is established, including: Initialize the given electric vehicle departure sequence table, driving energy consumption, and starting SOC information; Simulate a round of departure, and order the vehicles and equipment batteries from 1 to n et The remaining backup batteries are numbered in order from n et+1 to n bp serial number; Simulate the travel chain according to the departure schedule, and calculate the SOC of the battery carried by each electric vehicle when it returns to the station based on the driving distance and road conditions; Determine in sequence whether the battery pack needs to be charged and whether the vehicle needs to be charged. If the battery pack SOC meets the charging requirements, remove the battery pack and add it to the end of the spare battery pack sequence to queue for charging. At the same time, record the current SOC information of the battery pack. Load the fully charged batteries at the front of the spare battery group sequence into the electric vehicle, where the spare battery group sequence is arranged in ascending order of subscripts, and the one with the smallest subscript is at the front and is charged first, and then the vehicle is added to the departure sequence and waits for the next round of departure; By counting the SOC and time information of the replaced battery packs, combined with the scheduling of the number and power of the charging batteries and the matching strategy for the subsequent charging and swapping needs, the daily power change curve of the charging and swapping station can be obtained; Establish a battery charging rule matrix and a vehicle battery replacement demand matrix based on the behavior data set to make vehicle and battery matching decisions; The economic optimal upper model of the battery swap station is established, and the formula of the upper model is: in: The cost of purchasing electricity, is the configuration cost of battery energy storage, To participate in the sale of electricity for ancillary services, for carbon market revenues; and Considering that the load fluctuation of the battery swap station operation will have an impact on the microgrid, a lower-level model with the goal of minimizing the load fluctuation variance is established. The formula of the lower-level model is: Where: T is the scheduling period, P equ,t It is the real-time equivalent load power of the power station during the dispatching period.

2. The method for optimizing operation of a microgrid containing a battery swap station according to claim 1, characterized in that: The formula for establishing the electric vehicle travel chain model is: Among them, TC t is the travel chain of electric vehicle t; is the departure location of electric vehicle t in the i-th travel chain; is the departure time of electric vehicle t in the i-th trip chain; ΔM t,i is the mileage of electric vehicle t in the i-th travel chain; ΔT t,i is the driving time of electric vehicle t in the i-th travel chain; is the destination of electric vehicle t in the i-th travel chain; is the arrival time of electric vehicle t in the i-th travel chain; is the parking time of electric vehicle t after the end of the i-th travel chain.

3. The method for optimizing operation of a microgrid containing a battery swap station according to claim 2, characterized in that: The SOC formula of the electric vehicle is: Among them: SOC r,t is the SOC (%) of the electric vehicle r when it returns to the battery swap station at time t; SOC r,0 is the initial SOC value of the electric vehicle r at the beginning of the simulation process; S r is the nominal mileage of electric vehicle r; D r,t is the driving distance of electric vehicle r in the tth period; ΔSOC r,t is the SOC increment of electric vehicle r at time t. If it is not charged or replaced during the tth period, it is 0; η r,t is the battery capacity attenuation coefficient of electric vehicle r at time t, which can be simplified to 0.

4. The method for optimizing operation of a microgrid containing a battery swap station according to claim 3, characterized in that: According to the SOC state conversion function, the SOC of the electric vehicle r when it returns to the charging point at time t can be obtained, and combined with the expected travel distance in the next time period, the criterion for the battery pack and vehicle charging and swapping needs can be defined as: Among them, D r,t+1 is the expected travel distance of electric vehicle r at t+1; l r,t+1 The mileage margin reserved for electric vehicle r when considering whether to charge or replace the battery.

5. The method for optimizing operation of a microgrid containing a battery swap station according to claim 1, characterized in that: The steps of establishing a battery charging rule matrix and a vehicle battery replacement demand matrix based on the behavior data set and making matching decisions specifically include: establishing charging demand and battery replacement demand matching indicators, matching the two using the superior and inferior solution distance method and sorting method, obtaining a charging and battery replacement matching plan, and selecting the optimal battery pack and the corresponding numbered vehicle for charging and battery replacement operations.

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