Electric vehicle flexible charging and discharging analysis method and device considering user requirements

Abstract

The invention discloses an electric vehicle flexible charging and discharging analysis method and device considering user demands, and the method comprises the steps: constructing a flexible charging and discharging strategy model considering the user demands, and the flexible charging and discharging strategy model comprises a user travel time-battery electricity quantum model and a flexible charging and discharging potential evaluation sub-model; constraint conditions and an objective function are introduced into the flexible charging and discharging strategy model, the constraint conditions of the flexible charging and discharging strategy model comprise automobile charging and discharging constraint, electric automobile state of charge SOC constraint and automobile owner income constraint, and the objective function of the flexible charging and discharging strategy model aims at minimizing variance and maximizing user cost; and solving the flexible charging and discharging strategy model by using an improved Harris eagle optimization algorithm to obtain an electric vehicle flexible charging and discharging scheduling analysis result considering user demands. According to the method, the charging demand, the discharging loss and the charging and discharging income of an electric automobile owner are fully considered, flexible discharging can be achieved according to the load demand, and the purpose of peak load shifting is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization and dispatching, and in particular relates to a method and device for analyzing flexible charging and discharging of electric vehicles taking into account user needs. Background Art

[0002] Electric vehicles, with their environmentally friendly and zero-pollution characteristics, provide a reliable option for the development of green transportation systems. However, with the large-scale access of electric vehicles to the power system, their disorderly and uncoordinated charging behavior will lead to a significant increase in power load, thereby increasing the power supply pressure and operational stability of the power system supply side.

[0003] At present, dispatching technology cannot meet the supply and demand balance requirements of power systems containing electric vehicles. Therefore, in response to the above situation, traditional technology has proposed an electric vehicle coordinated charging model, which can dispatch electric vehicles to coordinate charging during low-power periods to smooth the load curve. However, the operating scenario of this model is too ideal and does not take into account the charging needs of users; and it does not give full play to the charging and discharging potential of electric vehicles as a new type of energy storage, making it difficult to alleviate the power supply pressure of the power system. Summary of the invention

[0004] To this end, the present invention provides an electric vehicle flexible charging and discharging analysis method and device taking into account user needs, which fully considers the charging needs of electric vehicle owners, so as to give full play to the charging and discharging potential of electric vehicles as a new type of energy storage and alleviate the power supply pressure of the power system.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a flexible charging and discharging analysis method for electric vehicles taking into account user needs, comprising:

[0006] Obtaining factors affecting charging and discharging of electric vehicles, wherein the factors affecting charging, factors affecting discharging, and factors affecting charging and discharging;

[0007] According to the acquired influencing factors of electric vehicle charging and discharging, a flexible charging and discharging strategy model taking into account user needs is constructed, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy consumption efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, vehicle discharge loss, discharge amount, electric vehicle owner discharge income and charging cost;

[0008] Introducing constraints and objective functions into the flexible charging and discharging strategy model, wherein the constraints of the flexible charging and discharging strategy model include vehicle charging and discharging constraints, electric vehicle state of charge (SOC) constraints, and vehicle owner revenue constraints, and the objective function of the flexible charging and discharging strategy model aims to minimize variance and maximize user cost;

[0009] The flexible charging and discharging strategy model is solved using the improved Harris Eagle optimization algorithm to obtain the flexible charging and discharging scheduling analysis results of electric vehicles taking into account user needs.

[0010] As a preferred scheme for the flexible charging and discharging analysis method of electric vehicles taking into account user needs, the factors affecting charging include user travel habits, weather, charging piles and electricity price policies; the factors affecting discharging include subsidy policies, battery characteristics and charging infrastructure; the factors affecting charging and discharging include vehicle usage patterns, initial state of charge and grid-side factors.

[0011] As a preferred solution for the flexible charging and discharging analysis method of electric vehicles taking into account user needs, the expression of the user travel time-battery power quantum model is:

[0012]

[0013] SOC ideal =(D×E+C-SOC now ×C)

[0014] Where F1(t) and F2(t) are the time when the electric vehicle arrives at and leaves the charging station, μ1 is 8, μ2 is 19, σ is 3, and t is the time when the electric vehicle may arrive and leave; SOC ideal and SOC now They represent the ideal battery power and current power respectively; D represents the future driving mileage, E represents the energy efficiency; and C represents the rated capacity.

[0015] As a preferred solution of the flexible charging and discharging analysis method for electric vehicles taking into account user needs, the expression of the flexible charging and discharging potential evaluation sub-model is:

[0016]

[0017] Where, I represents the comprehensive evaluation index of charge and discharge; λ is the vehicle discharge loss; E disc is the discharge amount; They represent the discharging benefit and charging cost of electric vehicle owners respectively.

[0018] As a preferred solution of the electric vehicle flexible charging and discharging analysis method taking into account user needs, the expression of the vehicle charging and discharging constraint is:

[0019]

[0020] In the formula, C ev (t) is the battery power of the electric vehicle at time t; P chr ev (t) is the charging power of the electric vehicle at time t; P dis ev (t) is the discharge power of the electric vehicle at time t, and the power value at time t is determined by the optimization algorithm; C min ev (t), C max ev (t) are the upper and lower limits of the battery power of the electric vehicle; P cmax ev (t), P cmin ev (t) are the upper and lower limits of the electric vehicle charging power; P dmax ev (t), P dmin ev (t) are the upper and lower limits of the electric vehicle discharge power, which are 30 kW and 5 kW respectively;

[0021] The expression of the electric vehicle state of charge SOC constraint is:

[0022] SOC c ≤SOC c,max

[0023] SOC dc,min ≤SOC dc

[0024] SOC EV,demand ≤SOC EV,out

[0025] In the formula, SOC c State of charge for charging electric vehicles; SOC c,max The maximum state of charge allowed when charging an electric vehicle; SOC dc State of charge for electric vehicles; SOC dc,min The minimum state of charge allowed when discharging an electric vehicle; SOC EV，demand The expected charge of electric vehicle users when traveling; SOC EV，out is the charge state of the electric vehicle at time t when it needs to travel;

[0026] The expression of the owner's profit constraint is:

[0027]

[0028] In the formula, c grid The price for purchasing electricity from external power companies.

[0029] As a preferred solution for the flexible charging and discharging analysis method of electric vehicles taking into account user needs, the objective function of the flexible charging and discharging strategy model is:

[0030]

[0031] P load (t) = P basic (t)+P EV (t)

[0032]

[0033] Where, F1 is the load curve variance; P load (t) is the load demand power at time t; is the average load power at time t; P basic (t) is the basic user load power; P EVc (t) is the charging power of the electric vehicle. If it is a negative value, it indicates the discharging power. F2 is the comprehensive income of the electric vehicle owner. f c The cost of battery replacement; They represent the discharge income and charging cost of the electric vehicle owner at time t, and are 0.4 and 0.3 respectively. is the discharge power at time t; Bc , Bd is the charge and discharge efficiency.

[0034] The present invention also provides an electric vehicle flexible charging and discharging analysis device taking into account user needs, comprising:

[0035] An influencing factor acquisition module is used to acquire influencing factors of charging and discharging of electric vehicles, wherein the influencing factors include factors affecting charging, factors affecting discharging, and factors affecting charging and discharging;

[0036] A flexible charging and discharging strategy model construction module is used to construct a flexible charging and discharging strategy model that takes into account user needs based on the acquired influencing factors of electric vehicle charging and discharging, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy consumption efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, vehicle discharge loss, discharge amount, electric vehicle owner discharge income and charging cost;

[0037] A constraint-objective function introduction module is used to introduce constraint conditions and objective functions into the flexible charge-discharge strategy model. The constraint conditions of the flexible charge-discharge strategy model include vehicle charge-discharge constraint, electric vehicle state of charge SOC constraint and vehicle owner benefit constraint. The objective function of the flexible charge-discharge strategy model aims to minimize variance and maximize user cost.

[0038] The flexible charging and discharging scheduling analysis result generation module is used to solve the flexible charging and discharging strategy model using the improved Harris Eagle optimization algorithm to obtain the electric vehicle flexible charging and discharging scheduling analysis result taking into account user needs.

[0039] As a preferred solution of the electric vehicle flexible charging and discharging analysis device taking into account user needs, in the influencing factor acquisition module:

[0040] The factors affecting charging include user travel habits, weather, charging piles and electricity price policies; the factors affecting discharging include subsidy policies, battery characteristics and charging infrastructure; the factors affecting charging and discharging include vehicle usage patterns, initial charge state and grid-side factors.

[0041] As a preferred solution of the electric vehicle flexible charging and discharging analysis device taking into account user needs, in the flexible charging and discharging strategy model construction module, the expression of the user travel time-battery power quantum model is:

[0042]

[0043] SOC ideal =(D×E+C-SOC now ×C)

[0044] Where F1(t) and F2(t) are the time when the electric vehicle arrives at and leaves the charging station, μ1 is 8, μ2 is 19, σ is 3, and t is the time when the electric vehicle may arrive and leave; SOC ideal and SOC now They represent the ideal battery capacity and current capacity respectively; D represents the future driving range, E represents the energy efficiency; C represents the rated capacity;

[0045] In the flexible charge and discharge strategy model building module, the expression of the flexible charge and discharge potential evaluation sub-model is:

[0046]

[0047] Where, I represents the comprehensive evaluation index of charge and discharge; λ is the vehicle discharge loss; E disc is the discharge amount; They represent the discharging benefit and charging cost of electric vehicle owners respectively.

[0048] As a preferred solution of the electric vehicle flexible charging and discharging analysis device taking into account user needs, the constraint condition-objective function is introduced into the module, and the expression of the vehicle charging and discharging constraint is:

[0049]

[0050] In the formula, C ev (t) is the battery power of the electric vehicle at time t; P chr ev (t) is the charging power of the electric vehicle at time t; P dis ev (t) is the discharge power of the electric vehicle at time t, and the power value at time t is determined by the optimization algorithm; C min ev (t), C max ev (t) are the upper and lower limits of the battery power of the electric vehicle; P cmax ev (t), P cmin ev (t) are the upper and lower limits of the electric vehicle charging power; P dmax ev (t), P dmin ev (t) are the upper and lower limits of the electric vehicle discharge power, which are 30 kW and 5 kW respectively;

[0051] The constraint condition-objective function is introduced into the module, and the expression of the electric vehicle state of charge SOC constraint is:

[0052] SOC c ≤SOC c,max

[0053] SOC dc,min ≤SOC dc

[0054] SOC EV,demand ≤SOC EV,out

[0055] In the formula, SOC c State of charge for charging electric vehicles; SOC c,max The maximum state of charge allowed when charging an electric vehicle; SOC dc State of charge for electric vehicles; SOC dc,min The minimum state of charge allowed when discharging an electric vehicle; SOC EV，demand The expected charge of electric vehicle users when traveling; SOC EV，out is the charge state of the electric vehicle at time t when it needs to travel;

[0056] The constraint condition-objective function is introduced into the module, and the expression of the owner's profit constraint is:

[0057]

[0058] In the formula, c grid The price of purchasing electricity from external power companies;

[0059] The constraint condition-objective function is introduced into the module, and the objective function of the flexible charge and discharge strategy model is:

[0060]

[0061] P load (t) = P basic (t)+P EV (t)

[0062]

[0063] Where, F1 is the load curve variance; P load (t) is the load demand power at time t; is the average load power at time t; P basic (t) is the basic user load power; P EVc (t) is the charging power of the electric vehicle. If it is a negative value, it indicates the discharging power. F2 is the comprehensive income of the electric vehicle owner. f c The cost of battery replacement; They represent the discharge income and charging cost of the electric vehicle owner at time t, and are 0.4 and 0.3 respectively. is the discharge power at time t; Bc , Bd is the charge and discharge efficiency.

[0064] The invention has the following advantages: obtaining influencing factors of electric vehicle charging and discharging, wherein the influencing factors include factors affecting charging, factors affecting discharging, and factors affecting charging and discharging; constructing a flexible charging and discharging strategy model taking into account user needs according to the acquired influencing factors of electric vehicle charging and discharging, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy consumption efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, vehicle discharge loss, discharge amount, electric vehicle owner's discharge income and charging cost; introducing constraints and objective functions into the flexible charging and discharging strategy model, wherein the constraints of the flexible charging and discharging strategy model include vehicle charging and discharging constraints, electric vehicle state of charge SOC constraints and owner income constraints, and the objective function of the flexible charging and discharging strategy model takes minimizing variance and maximizing user cost as the goal; solving the flexible charging and discharging strategy model using an improved Harris Eagle optimization algorithm to obtain flexible charging and discharging scheduling analysis results of electric vehicles taking into account user needs. The present invention fully considers the charging needs, discharge losses and charging and discharging benefits of electric vehicle owners, making up for the shortcomings of traditional technologies; constructs a flexible charging and discharging strategy model, which can not only coordinate charging of electric vehicles, but also flexibly discharge according to load demand to achieve the purpose of peak shaving and valley filling; and effectively alleviates the power supply pressure of the output units. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0066] Figure 1 A schematic flow chart of a flexible charging and discharging analysis method for electric vehicles taking into account user needs provided in an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of factors affecting the charging and discharging of an electric vehicle in the electric vehicle flexible charging and discharging analysis method taking into account user needs provided in an embodiment of the present invention;

[0068] Figure 3 A flexible charging and discharging strategy model architecture diagram in an electric vehicle flexible charging and discharging analysis method taking into account user needs provided in an embodiment of the present invention;

[0069] Figure 4An example of a flexible charging and discharging analysis method for electric vehicles taking into account user needs provided in an embodiment of the present invention includes a microgrid power balance and output diagrams of each unit;

[0070] Figure 5 A simulation effect diagram of a flexible charging and discharging strategy of an electric vehicle flexible charging and discharging analysis method taking into account user needs provided in an embodiment of the present invention;

[0071] Figure 6 This is an architecture diagram of an electric vehicle flexible charging and discharging analysis device that takes user needs into consideration and is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Example 1

[0074] See also Figure 1 , Figure 2 and Figure 3 The embodiment of the present invention provides an electric vehicle flexible charging and discharging analysis method taking into account user needs, comprising the following steps:

[0075] S1. Obtaining factors affecting the charging and discharging of electric vehicles, wherein the factors affecting the charging, the factors affecting the discharging, and the factors affecting the charging and discharging;

[0076] S2. Based on the acquired influencing factors of electric vehicle charging and discharging, a flexible charging and discharging strategy model taking into account user needs is constructed, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include the comprehensive evaluation index of charging and discharging, the vehicle discharge loss, the discharge amount, the electric vehicle owner's discharge income and the charging cost;

[0077] S3, introducing constraints and objective functions into the flexible charging and discharging strategy model, wherein the constraints of the flexible charging and discharging strategy model include vehicle charging and discharging constraints, electric vehicle state of charge (SOC) constraints, and owner benefit constraints, and the objective function of the flexible charging and discharging strategy model aims to minimize variance and maximize user cost;

[0078] S4. Use the improved Harris Eagle optimization algorithm to solve the flexible charging and discharging strategy model to obtain the flexible charging and discharging scheduling analysis results of electric vehicles taking into account user needs.

[0079] In this embodiment, in step S1, the factors affecting charging include user travel habits, weather, charging piles and electricity price policies; the factors affecting discharging include subsidy policies, battery characteristics and charging infrastructure; the factors affecting charging and discharging include vehicle usage patterns, initial charge state and grid-side factors.

[0080] See also Figure 2 Specifically, among the factors affecting charging, the demand for charging is significantly different in terms of user travel habits (travel time, mileage, parking duration, etc.). At the same time, on different dates and at different times, the charging behavior of users has a certain randomness in time and space, which brings certain difficulties to the construction of charging behavior models and flexible charging and discharging models. In terms of weather factors, including temperature, humidity, sunshine, sunny or rainy days, etc., all affect the energy consumption and charging demand of electric vehicles. For example, in the hot summer, the car air conditioner should always operate at high load, which consumes a lot of energy, and the user's charging demand is also large, and the charging amount needs to be increased to ensure the endurance; on rainy days, the battery performance will decline, and the user's charging demand will also increase; in addition, the distribution, number, and charging power of charging piles will affect the charging demand of different groups. For residential areas, the demand for electric vehicle charging is relatively large at night, and economic slow charging at night will encourage users to charge, while in commercial areas, users have a greater demand for fast charging. In addition, the availability and uneven distribution of charging piles may lead to uneven distribution of charging demand in space; finally, the electricity price policy, the time-of-use electricity price policy has a greater impact on the user's charging demand. By implementing a time-of-use electricity price policy, users can be guided to charge during peak and off-peak load periods, thereby achieving the effect of peak shaving and valley filling.

[0081] Specifically, among the factors that affect discharge, the first is the subsidy factor. Vehicle-to-Grid (V2G) technology allows electric vehicles to interact with the power grid, but this depends on the subsidy policy adopted by charging station operators to stimulate users' discharge needs and enthusiasm. The second is battery characteristics: There are many types of batteries in existing electric vehicles, such as ternary lithium, lithium iron phosphate, etc., and their battery cycle times and charge and discharge loss characteristics are not the same. Users' consideration of battery depreciation and automobile manufacturers' battery warranty policies will also affect the willingness of electric vehicles to reverse charge to a certain extent; finally, the charging infrastructure. V2G charging piles are key connectors for vehicle-grid interaction. Their cost and popularity are one of the bottlenecks in promoting reverse charging, which will directly determine whether users can reverse charge.

[0082] Specifically, among the factors that affect charging and discharging, one is that the vehicle usage mode (commuting, leisure travel or commercial use, etc.) will affect the charging and discharging needs. Different usage modes will lead to different charging times, different charging amounts, and different reverse discharge intentions. The second is the initial state of charge (State of charger, SOC). The lower the initial charge state, the higher the possibility of charging behavior and the longer the charging time; conversely, the higher the initial charge state, the higher the possibility of reverse charging; finally, the grid-side factors, the load management and peak-shaving needs of the grid are important factors in promoting the bidirectional charging of electric vehicles. During peak hours, the grid needs additional power supply, and reverse charging of electric vehicles can provide a certain amount of power supply; during low-power periods, the grid has more surplus power, so electric vehicle charging can be used to consume electricity.

[0083] In this embodiment, in step S2, the needs of the car owner, such as travel habits, range concerns, discharge loss, etc., are focused on, and a flexible charging and discharging strategy model is constructed, aiming to flexibly mobilize electric vehicles to charge during low electricity consumption periods and discharge during peak electricity consumption periods, thereby achieving the effect of peak shaving and valley filling.

[0084] Among them, there are generally two charging modes for electric vehicles: home mode and work mode. In the work mode, the owner drives the vehicle when commuting and charges it at work. Taking the work mode as an example, assuming that the travel time of electric vehicles satisfies the normal distribution, the arrival and departure times of electric vehicles are generated, so in the user travel time-battery power quantum model:

[0085]

[0086] Where F1(t) and F2(t) are the times when the electric vehicle arrives at and leaves the charging station, μ1 is 8, μ2 is 19, σ is 3, and t is the time when the electric vehicle may arrive and leave.

[0087] The ideal charging amount of the car owner is related to the car owner's future mileage and the current battery power. Therefore, in the user travel time-battery power quantum model:

[0088] SOC ideal =(D×E+C-SOC now ×C) (2)

[0089] SOC ideal and SOC now They represent the ideal battery power and current power respectively; D represents the future driving mileage, E represents the energy efficiency; and C represents the rated capacity.

[0090] The time from when an electric vehicle arrives at a charging station to when it leaves the charging station is the dispatchable time. The manager collects the power consumption information of the power grid, time-of-use electricity prices and the demand information of charging owners, and dispatches the electric vehicle to flexibly charge and discharge within this time period, ultimately ensuring that the electric vehicle meets the owner's ideal power demand when leaving the charging station.

[0091] In this embodiment, in step S2, a flexible charging and discharging potential evaluation sub-model is constructed according to the owner's ideal power demand, discharge loss and discharge benefit. The charging station manager dispatches the electric vehicles in the charging station to complete the corresponding charging and discharging behaviors according to the flexible charging and discharging potential evaluation sub-model. The expression of the flexible charging and discharging potential evaluation sub-model is:

[0092]

[0093] Where, I represents the comprehensive evaluation index of charge and discharge; λ is the vehicle discharge loss; E disc is the discharge amount; They represent the discharging benefit and charging cost of electric vehicle owners respectively.

[0094] Among them, the charging station manager judges the charging and discharging behavior of the electric vehicle by the positive or negative value of I. If it is a negative value, it means that the owner's willingness to charge is greater than the willingness to discharge, and the current behavior is charging. At this time, if I is too small, the car will be dispatched to charge immediately; on the contrary, it can be dispatched to charge during the off-peak period of electricity consumption, and finally ensure that the ideal power demand is met before leaving the charging station. If I is a positive value, it means that the willingness to discharge is greater than the willingness to charge. The manager can dispatch the car to discharge during the peak period of electricity consumption to relieve the power supply pressure of the power grid.

[0095] In this embodiment, in step S3, the expression of the vehicle charging and discharging constraint is:

[0096]

[0097] In the formula, C ev (t) is the battery power of the electric vehicle at time t; P chr ev (t) is the charging power of the electric vehicle at time t; P dis ev (t) is the discharge power of the electric vehicle at time t, and the power value at time t is determined by the optimization algorithm; C min ev (t), C max ev (t) are the upper and lower limits of the battery power of the electric vehicle; P cmax ev (t), P cmin ev (t) are the upper and lower limits of the electric vehicle charging power; Pdmax ev (t), P dmin ev (t) are the upper and lower limits of the electric vehicle discharge power, which are 30 kW and 5 kW respectively;

[0098] The expression of the electric vehicle state of charge SOC constraint is:

[0099] SOC c ≤SOC c,max (5)

[0100] SOC dc,min ≤SOC dc (6)

[0101] SOC EV,demand ≤SOC EV,out (7)

[0102] In the formula, SOC c State of charge for charging electric vehicles; SOC c,max The maximum state of charge allowed when charging an electric vehicle; SOC dc State of charge for electric vehicles; SOC dc,min The minimum state of charge allowed when discharging an electric vehicle; SOC EV，demand The expected charge of electric vehicle users when traveling; SOC EV，out is the charge state of the electric vehicle at time t when it needs to travel;

[0103] In step S3, the expression of the owner's profit constraint is:

[0104]

[0105] In the formula, c grid The price for purchasing electricity from external power companies.

[0106] In step S3, based on the flexible charge-discharge strategy model and the constraint conditions, the objective function of the flexible charge-discharge strategy model is:

[0107]

[0108] P load (t) = P basic (t)+P EV (t) (10)

[0109]

[0110] Where, F1 is the load curve variance; P load (t) is the load demand power at time t; is the average load power at time t; P basic (t) is the basic user load power; P EVc (t) is the charging power of the electric vehicle. If it is a negative value, it indicates the discharging power. F2 is the comprehensive income of the electric vehicle owner. f c The cost of battery replacement; They represent the discharge income and charging cost of the electric vehicle owner at time t, and are 0.4 and 0.3 respectively. is the discharge power at time t; Bc , Bd is the charge and discharge efficiency.

[0111] In this embodiment, in step S4, the Harris Hawks optimization algorithm (HHO) is improved, and then an external warehouse mechanism is introduced to make HHO a multi-objective optimization algorithm for solving the flexible charging and discharging strategy model.

[0112] Specifically, the principle, improvement points of the Harris Eagle optimization algorithm used, and the introduction of the external warehouse mechanism are as follows:

[0113] 1) Initialization

[0114] In HHO, the initialization position of each eagle is expressed by equation (12):

[0115]

[0116] Among them, X h is the position vector of the hth eagle, each column represents the coordinates on the corresponding dimension; dim represents the dimension of the optimization problem; X lb and X ub Respectively represent the lower and upper limits of the search range, both of which are row vectors; r d It is a random number between [0,1], rd(1,dim) means generating 1 row, dim column r d A vector of random numbers. ⊕ denotes entry-wise multiplication.

[0117] After each eagle initializes its position, the position matrix X pop as follows:

[0118]

[0119] Where N represents the number of eagles in the search space, that is, the population size.

[0120] 2) Escape Energy (E s )

[0121] In HHO, the escape energy E s is used to control the transition from exploration to development, Es From 2 to 0 linearly, the escape energy E s Calculated from E1 and E0:

[0122] E s =E0·E1 (14)

[0123] In the formula, E1 is a factor used to simulate the decrease of the rabbit's escape energy as the number of iterations increases. E0 is a random number between [-1,1] used to increase the randomness of the escape energy. The definitions of E1 and E0 are as follows:

[0124]

[0125] In the formula, t represents the current iteration number, T Max Indicates the maximum number of iterations; rd2 is a random number between 0 and 1.

[0126] 3) Global exploration phase (random habitat phase, |Es| ≥ 1)

[0127] This stage simulates the search behavior of an eagle preying on rabbits. Formula (16) specifically describes the two different strategy selection behaviors of the eagle, that is, the eagle will randomly perch in some locations to select strategies according to the random number d (a random number between [0,1]), and wait to find rabbits according to the corresponding strategy:

[0128]

[0129] Where, X r Indicates that each iteration in the position matrix X pop The eagle randomly selected from the corresponding position; rd3, rd4, rd5, rd6 are all random numbers between 0 and 1; R i (t) represents the position of the rabbit, which is the optimal solution currently found; X i (t) represents the current position of the eagle.

[0130] 4) Local development phase (attack phase)

[0131] The hawk performs a raid by attacking the target rabbit found in the previous stage. However, rabbits often try to escape from dangerous situations. In the attack phase of the HHO model, four possible strategies are proposed based on the escape behavior of the rabbit and the pursuit strategy of the hawk. Let p be the probability that the rabbit successfully escapes (p<0.5) or fails to escape (p≥0.5) before the raid.

[0132] a. Attack stage (p ≥ 0.5 and |E s |<0.5)

[0133] At this stage, the rabbit is exhausted and has lost energy. sThe eagle does not surround the intended rabbit and executes a surprise attack strategy. The new position of the eagle at this stage is given by formula (17):

[0134] X i (t+1)=R i (t)-E s ·|R i (t)-X i (t)| (17)

[0135] b. Rapid dive (p ≥ 0.5 and |E s |≥0.5)

[0136] At this stage, the rabbit still has enough energy and tries to escape by some random misleading jumps, but ultimately fails. During these attempts, the hawk surrounds the rabbit, making it more tired, and then executes the pounce. The position of the hawk is given by equation (18):

[0137]

[0138] Where J s is a random number between 0 and 2, simulating the nature of the rabbit's movement; the Levy flight function is used to imitate the rabbit's deceptive movement during the escape phase and the eagle's irregular, sudden and rapid dives around the rabbit when chasing it. The process can be expressed by formula (19):

[0139]

[0140] Where μ, ν are random values ​​in [0, 1], and β is a default constant value of 1.5.

[0141] c. Progressive soft bracketing (p<0.5 and |E s |≥0.5)

[0142] In this stage, the eagle makes several quick flights around the rabbit and tries to gradually correct its position and direction according to the rabbit's deceptive movements. The position of the eagle is given by equation (20):

[0143]

[0144] Where f(X) is the fitness function. In the flexible charging and discharging model of electric vehicles taking into account user needs, f(X) is the objective function defined by two operators.

[0145] In single-objective optimization, formula (20) is used to compare the new positions X1, X2 expected to be reached by the eagle and the current position X i In multi-objective optimization, roulette or other methods should be used to select new positions from the non-dominated solution set to replace equation (20).

[0146] The new position of the eagle X1, X2 is expressed by equation (21):

[0147]

[0148] d. Progressive hard bracketing (p < 0.5 and |E s |<0.5)

[0149] The rabbit does not have enough energy to escape, and the hawk builds a hard encirclement before the pounce to capture the prey. But in this stage, the hawk tries to reduce the average distance between them and the escaping rabbit. The position of the hawk is given by equation (22):

[0150]

[0151] In the formula, X3 and X4 are the new positions that the eagle is expected to reach, and the calculation is as follows:

[0152]

[0153] In the formula, Represents the average position of the eagle to provide more diverse trends and explore the feature space of different regions to imitate the behavior of the eagle. It is calculated as follows:

[0154]

[0155] (5) Improvement of progressive hard encirclement during the development phase

[0156] Since the original progressive hard encirclement development method is similar to the soft encirclement form and the development intensity is insufficient, the improved method improves the encirclement capability, that is, the development intensity, to improve the quality of the optimal solution. The improved eagle's expected arrival position X3 is shown in formula (25):

[0157]

[0158] (6) Introducing an external warehouse mechanism makes HHO a multi-objective optimization algorithm

[0159] Multi-objective optimization cannot judge the relationship between solutions by comparing the size of fitness values, because there is no global optimal solution for multi-objective optimization problems, there is only a set of better non-dominated solutions, and the non-dominated solution set needs to be stored (here an external warehouse mechanism is introduced). The selection of non-dominated solutions is shown in the following formula:

[0160]

[0161] After judging by formula (26), the non-dominated solution set is stored.

[0162] The external warehouse mechanism will perform warehouse maintenance based on the congestion distance. When the quantity stored in the warehouse exceeds the set capacity, the non-dominated solution with the largest congestion will be eliminated until the warehouse capacity is normal. The method for selecting the eliminated non-dominated solutions is as follows:

[0163]

[0164] In the formula, Indicates the i-th solution selected and removed in the external archive, Num i Represents the number of adjacent solutions around the ith solution, which is an integer greater than 1.

[0165] Among them, the workflow of the external archiving mechanism can be summarized as follows:

[0166] a. Set the storage capacity of the external warehouse (the maximum number of non-dominated solution sets)

[0167] b. Use equation (26) to determine the non-dominated solution. When the new solution is a non-dominated solution, store the new solution in an external archiving mechanism;

[0168] c. When the new solution is dominated by other solutions, eliminate the new solution;

[0169] d. If the capacity of the external warehouse reaches the threshold, the selected non-dominated solutions are eliminated according to formula (27), and solutions that meet the archiving requirements are reselected and stored in the external warehouse, so that the number of non-dominated solutions just reaches the archiving capacity threshold.

[0170] The above introduces the principle of the HHO algorithm. The introduction of the improved external warehouse mechanism enables it to have the ability of multi-objective optimization. It can simultaneously optimize and solve the two objective functions of equations (9) and (11) to obtain the final scheduling result.

[0171] In order to verify the effect of the present invention, a microgrid model containing electric vehicles was constructed in the simulation software. The flexible charging and discharging of electric vehicles was scheduled based on the constructed flexible charging and discharging model of electric vehicles taking into account user needs. The scheduling results are as follows: Figure 4 As shown. Figure 4 It can be seen that the red line represents the load curve before optimization, and the yellow line represents the load curve after optimization. Electric vehicles began to connect to charging stations in large numbers at 8:00, causing a peak in electricity consumption between 8:00 and 9:00. Through the flexible charging and discharging strategy for electric vehicles that takes into account user needs, the charging time is shifted to 12:00 and 13:00 when electricity consumption is low, and the amount of electricity transferred reaches 560kW, achieving the effect of peak shaving and valley filling. At the same time, for some dischargeable electric vehicles, the system dispatches them to reverse power supply to the system during peak electricity consumption to alleviate the pressure on the system power supply.

[0172] from Figure 5It can be seen that the charging demand is coordinated and transferred to 10:00-11:00 and 13:00. For electric vehicles that can discharge, they are scheduled to discharge during the peak period of 8:00-9:00, with a discharge capacity of 906kW, which effectively alleviates the power supply pressure of the output units.

[0173] In summary, the present invention obtains influencing factors of electric vehicle charging and discharging, and the influencing factors include factors affecting charging, factors affecting discharging, and factors affecting charging and discharging; according to the influencing factors of electric vehicle charging and discharging obtained, a flexible charging and discharging strategy model taking into account user needs is constructed, and the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future mileage, the energy efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, a vehicle discharge loss, a discharge amount, an electric vehicle owner's discharge income and a charging cost; constraints and objective functions are introduced into the flexible charging and discharging strategy model, and the constraints of the flexible charging and discharging strategy model include vehicle charging and discharging constraints, electric vehicle state of charge SOC constraints and owner income constraints, and the objective function of the flexible charging and discharging strategy model aims to minimize variance and maximize user costs; the flexible charging and discharging strategy model is solved using an improved Harris Eagle optimization algorithm to obtain an electric vehicle flexible charging and discharging scheduling analysis result taking into account user needs. The present invention fully considers the charging needs, discharge losses and charging and discharging benefits of electric vehicle owners, making up for the shortcomings of traditional technologies; constructs a flexible charging and discharging strategy model, which can not only coordinate charging of electric vehicles, but also flexibly discharge according to load demand to achieve the purpose of peak shaving and valley filling; and effectively alleviates the power supply pressure of the output units.

[0174] Example 2

[0175] See also Figure 6 Embodiment 2 of the invention further provides an electric vehicle flexible charging and discharging analysis device taking into account user needs, comprising:

[0176] The influencing factor acquisition module 100 is used to acquire the influencing factors of charging and discharging of the electric vehicle, wherein the influencing factors include factors affecting charging, factors affecting discharging, and factors affecting charging and discharging;

[0177] The flexible charging and discharging strategy model construction module 200 is used to construct a flexible charging and discharging strategy model taking into account user needs according to the acquired influencing factors of electric vehicle charging and discharging, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy consumption efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, vehicle discharge loss, discharge amount, electric vehicle owner's discharge income and charging cost;

[0178] A constraint-objective function introduction module 300 is used to introduce constraint conditions and objective functions into the flexible charge-discharge strategy model. The constraint conditions of the flexible charge-discharge strategy model include vehicle charge-discharge constraint, electric vehicle state of charge SOC constraint and vehicle owner benefit constraint. The objective function of the flexible charge-discharge strategy model aims to minimize variance and maximize user cost.

[0179] The flexible charge and discharge scheduling analysis result generating module 400 is used to solve the flexible charge and discharge strategy model using the improved Harris Eagle optimization algorithm to obtain the electric vehicle flexible charge and discharge scheduling analysis result taking into account user needs.

[0180] In this embodiment, in the influencing factor acquisition module 100:

[0181] The factors affecting charging include user travel habits, weather, charging piles and electricity price policies; the factors affecting discharging include subsidy policies, battery characteristics and charging infrastructure; the factors affecting charging and discharging include vehicle usage patterns, initial charge state and grid-side factors.

[0182] In this embodiment, in the flexible charging and discharging strategy model building module 200, the expression of the user travel time-battery power quantum model is:

[0183]

[0184] SOC ideal =(D×E+C-SOC now ×C)

[0185] Where F1(t) and F2(t) are the time when the electric vehicle arrives at and leaves the charging station, μ1 is 8, μ2 is 19, σ is 3, and t is the time when the electric vehicle may arrive and leave; SOC ideal and SOC now They represent the ideal battery capacity and current capacity respectively; D represents the future driving range, E represents the energy efficiency; C represents the rated capacity;

[0186] In the flexible charge and discharge strategy model building module 200, the expression of the flexible charge and discharge potential evaluation sub-model is:

[0187]

[0188] Where, I represents the comprehensive evaluation index of charge and discharge; λ is the vehicle discharge loss; E disc is the discharge amount; They represent the discharging benefit and charging cost of electric vehicle owners respectively.

[0189] In this embodiment, the constraint condition-objective function is introduced into the module 300, and the expression of the vehicle charging and discharging constraint is:

[0190]

[0191] In the formula, C ev (t) is the battery power of the electric vehicle at time t; P chr ev (t) is the charging power of the electric vehicle at time t; P dis ev (t) is the discharge power of the electric vehicle at time t, and the power value at time t is determined by the optimization algorithm; C min ev (t), C max ev (t) are the upper and lower limits of the battery power of the electric vehicle; P cmax ev (t), P cmin ev (t) are the upper and lower limits of the electric vehicle charging power; P dmax ev (t), P dmin ev (t) are the upper and lower limits of the electric vehicle discharge power, which are 30 kW and 5 kW respectively;

[0192] The constraint condition-objective function is introduced into the module 300, and the expression of the electric vehicle state of charge SOC constraint is:

[0193] SOC c ≤SOC c,max

[0194] SOC dc,min ≤SOC dc

[0195] SOC EV,demand ≤SOC EV,out

[0196] In the formula, SOCc State of charge for charging electric vehicles; SOC c,max The maximum state of charge allowed when charging an electric vehicle; SOC dc State of charge for electric vehicles; SOC dc,min The minimum state of charge allowed when discharging an electric vehicle; SOC EV，demand The expected charge of electric vehicle users when traveling; SOC EV，out is the charge state of the electric vehicle at time t when it needs to travel;

[0197] The constraint condition-objective function is introduced into module 300, and the expression of the owner's profit constraint is:

[0198]

[0199] In the formula, c grid The price of purchasing electricity from external power companies;

[0200] The constraint condition-objective function is introduced into the module, and the objective function of the flexible charge and discharge strategy model is:

[0201]

[0202] P load (t) = P basic (t)+P EV (t)

[0203]

[0204] Where, F1 is the load curve variance; P load (t) is the load demand power at time t; is the average load power at time t; P basic (t) is the basic user load power; P EVc (t) is the charging power of the electric vehicle. If it is a negative value, it indicates the discharging power. F2 is the comprehensive income of the electric vehicle owner. f c The cost of battery replacement; They represent the discharge income and charging cost of the electric vehicle owner at time t, and are 0.4 and 0.3 respectively. is the discharge power at time t; Bc , Bd is the charge and discharge efficiency.

[0205] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0206] Example 3

[0207] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for an electric vehicle flexible charging and discharging analysis method taking into account user needs is stored, and the program code includes instructions for executing embodiment 1 or any possible implementation method of an electric vehicle flexible charging and discharging analysis method taking into account user needs.

[0208] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk, SSD), etc.

[0209] Example 4

[0210] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0211] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute an electric vehicle flexible charging and discharging analysis method that takes into account user needs in embodiment 1 or any possible implementation thereof.

[0212] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0213] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0214] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0215] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A flexible charging and discharging analysis method for electric vehicles taking into account user needs, characterized in that: include: Obtaining factors affecting charging and discharging of electric vehicles, wherein the factors affecting charging, factors affecting discharging, and factors affecting charging and discharging; According to the acquired influencing factors of electric vehicle charging and discharging, a flexible charging and discharging strategy model taking into account user needs is constructed, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy consumption efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, vehicle discharge loss, discharge amount, electric vehicle owner discharge income and charging cost; Introducing constraints and objective functions into the flexible charging and discharging strategy model, wherein the constraints of the flexible charging and discharging strategy model include vehicle charging and discharging constraints, electric vehicle state of charge (SOC) constraints, and vehicle owner revenue constraints, and the objective function of the flexible charging and discharging strategy model aims to minimize variance and maximize user cost; The flexible charging and discharging strategy model is solved using the improved Harris Eagle optimization algorithm to obtain the flexible charging and discharging scheduling analysis results of electric vehicles taking into account user needs.

2. The electric vehicle flexible charging and discharging analysis method taking into account user needs according to claim 1, characterized in that: The factors affecting charging include user travel habits, weather, charging piles and electricity price policies; the factors affecting discharging include subsidy policies, battery characteristics and charging infrastructure; the factors affecting charging and discharging include vehicle usage patterns, initial charge state and grid-side factors.

3. The electric vehicle flexible charging and discharging analysis method taking into account user needs according to claim 1, characterized in that: The expression of the user travel time-battery power quantum model is: SOCIETY ideal =(D×E+C-SOC now ×C) Where F1(t) and F2(t) are the time when the electric vehicle arrives at and leaves the charging station, μ1 is 8, μ2 is 19, σ is 3 (the claim does not need to write the value, but explain the meaning of the parameter, please modify), t is the time when the electric vehicle may arrive and leave; SOC ideal and SOC now They represent the ideal battery power and current power respectively; D represents the future driving mileage, E represents the energy efficiency; and C represents the rated capacity.

4. The electric vehicle flexible charging and discharging analysis method taking into account user needs according to claim 3 is characterized in that: The expression of the flexible charge and discharge potential evaluation sub-model is: Where, I represents the comprehensive evaluation index of charge and discharge; λ is the vehicle discharge loss; E disc is the discharge amount; They represent the discharging benefit and charging cost of electric vehicle owners respectively.

5. The electric vehicle flexible charging and discharging analysis method taking into account user needs according to claim 4, characterized in that: The expression of the vehicle charging and discharging constraint is: In the formula, C ev (t) is the battery power of the electric vehicle at time t; P chr ev (t) is the charging power of the electric vehicle at time t; P dis ev (t) is the discharge power of the electric vehicle at time t, and the power value at time t is determined by the optimization algorithm; C min ev (t), C max ev (t) are the upper and lower limits of the battery power of the electric vehicle; P cmax ev (t), P cmin ev (t) are the upper and lower limits of the electric vehicle charging power; P dmax ev (t), P dmin ev (t) are the upper and lower limits of the electric vehicle discharge power respectively; The expression of the electric vehicle state of charge SOC constraint is: SOC c ≤SOC c,max SOC dc,min ≤SOC dc SOC EV,demand ≤SOC EV,out In the formula, SOC c State of charge for charging electric vehicles; SOC c,max The maximum state of charge allowed when charging an electric vehicle; SOC dc State of charge for electric vehicles; SOC dc,min The minimum state of charge allowed when discharging an electric vehicle; SOC EV，demand The expected charge for electric vehicle users when traveling; SOC EV，ou is the charge state of the electric vehicle at time t when it needs to travel; The expression of the owner's profit constraint is: In the formula, c grid The price for purchasing electricity from external power companies.

6. The electric vehicle flexible charging and discharging analysis method taking into account user needs according to claim 5, characterized in that: The objective function of the flexible charge and discharge strategy model is: P load (t)=P basic (t)+P EV (t) Where, F1 is the load curve variance; P load (t) is the load demand power at time t; is the average load power at time t; P basic (t) is the basic user load power; P EVc (t) is the charging power of the electric vehicle. If it is a negative value, it indicates the discharging power. F2 is the comprehensive income of the electric vehicle owner. f c The cost of battery replacement; They represent the discharge income and charging cost of the electric vehicle owner at time t, is the discharge power at time t; λ Bc , Bd is the charge and discharge efficiency.

7. A flexible charging and discharging analysis device for electric vehicles taking into account user needs, characterized in that: include: An influencing factor acquisition module is used to acquire influencing factors of charging and discharging of electric vehicles, wherein the influencing factors include factors affecting charging, factors affecting discharging, and factors affecting charging and discharging; A flexible charging and discharging strategy model construction module is used to construct a flexible charging and discharging strategy model that takes into account user needs based on the acquired influencing factors of electric vehicle charging and discharging, wherein the flexible charging and discharging strategy model includes a user travel time-battery power sub-model and a flexible charging and discharging potential evaluation sub-model; the parameters of the user travel time-battery power sub-model include the time when the electric vehicle arrives at and leaves the charging station, the ideal battery power and the current power, the future driving mileage, the energy consumption efficiency and the rated capacity; the parameters of the flexible charging and discharging potential evaluation sub-model include a comprehensive charging and discharging evaluation index, vehicle discharge loss, discharge amount, electric vehicle owner discharge income and charging cost; A constraint-objective function introduction module is used to introduce constraint conditions and objective functions into the flexible charge-discharge strategy model. The constraint conditions of the flexible charge-discharge strategy model include vehicle charge-discharge constraint, electric vehicle state of charge SOC constraint and vehicle owner benefit constraint. The objective function of the flexible charge-discharge strategy model aims to minimize variance and maximize user cost. The flexible charging and discharging scheduling analysis result generation module is used to solve the flexible charging and discharging strategy model using the improved Harris Eagle optimization algorithm to obtain the electric vehicle flexible charging and discharging scheduling analysis result taking into account user needs.

8. The electric vehicle flexible charging and discharging analysis device taking into account user needs according to claim 7, characterized in that: In the influencing factor acquisition module: The factors affecting charging include user travel habits, weather, charging piles and electricity price policies; the factors affecting discharging include subsidy policies, battery characteristics and charging infrastructure; the factors affecting charging and discharging include vehicle usage patterns, initial charge state and grid-side factors.

9. The electric vehicle flexible charging and discharging analysis device taking into account user needs according to claim 8, characterized in that: In the flexible charging and discharging strategy model building module, the expression of the user travel time-battery power quantum model is: SOCIETY ideal =(D×E+C-SOC now ×C) Where F1(t) and F2(t) are the time when the electric vehicle arrives at and leaves the charging station, μ1 is 8, μ2 is 19, σ is 3, and t is the time when the electric vehicle may arrive and leave; SOC ideal and SOC now They represent the ideal battery capacity and current capacity respectively; D represents the future driving range, E represents the energy efficiency; C represents the rated capacity; In the flexible charge and discharge strategy model building module, the expression of the flexible charge and discharge potential evaluation sub-model is: Where, I represents the comprehensive evaluation index of charge and discharge; λ is the vehicle discharge loss; E disc is the discharge amount; They represent the discharging benefit and charging cost of electric vehicle owners respectively.

10. The electric vehicle flexible charging and discharging analysis device taking into account user needs according to claim 9, characterized in that: The constraint condition-objective function is introduced into the module, and the expression of the vehicle charging and discharging constraint is: In the formula, C ev (t) is the battery power of the electric vehicle at time t; P chr ev (t) is the charging power of the electric vehicle at time t; P dis ev (t) is the discharge power of the electric vehicle at time t, and the power value at time t is determined by the optimization algorithm; C min ev (t), C max ev (t) are the upper and lower limits of the battery power of the electric vehicle; P cmax ev (t), P cmin ev (t) are the upper and lower limits of the electric vehicle charging power; P dmax ev (t), P dmin ev (t) are the upper and lower limits of the electric vehicle discharge power respectively; The constraint condition-objective function is introduced into the module, and the expression of the electric vehicle state of charge SOC constraint is: SOC c ≤SOC c,max SOC dc,min ≤SOC dc SOC EV,demand ≤SOC EV,out In the formula, SOC c State of charge for charging electric vehicles; SOC c,max The maximum state of charge allowed when charging an electric vehicle; SOC dc State of charge for electric vehicles; SOC dc,min The minimum state of charge allowed when discharging an electric vehicle; SOC EV，demand The expected charge of electric vehicle users when traveling; SOC EV，out is the charge state of the electric vehicle at time t when it needs to travel; The constraint condition-objective function is introduced into the module, and the expression of the owner's profit constraint is: In the formula, c grid The price of purchasing electricity from external power companies; The constraint condition-objective function is introduced into the module, and the objective function of the flexible charge and discharge strategy model is: P load (t)=P basic (t)+P EV (t) Where, F1 is the load curve variance; P load (t) is the load demand power at time t; is the average load power at time t; P basic (t) is the basic user load power; P EVc (t) is the charging power of the electric vehicle. If it is a negative value, it indicates the discharging power. F2 is the comprehensive income of the electric vehicle owner. f c The cost of battery replacement; They represent the discharge income and charging cost of the electric vehicle owner at time t, is the discharge power at time t; λ Bc , Bd is the charge and discharge efficiency.

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