Electric vehicle charging and discharging control method and system

Abstract

The invention discloses an electric vehicle charging and discharging control method and system, and relates to the technical field of electric vehicle charging and discharging control, and the method comprises the steps: constructing a first discharging scheduling model and a second discharging scheduling model according to the discharging capability and the charging cost; performing optimization analysis on the first charging and discharging model and the second charging and discharging model, and formulating different charging and discharging strategies of the electric vehicle; and formulating a charging mode according to the charging and discharging strategy and a user demand scene, and optimizing the charging and discharging strategy according to a simulation result. According to the method, the intelligence and automation level of the charging and discharging process of the electric vehicle is improved, the manual intervention cost is reduced, the stability and economical efficiency of power grid operation are improved by optimizing the charging and discharging strategy, different user requirements are met, the flexibility and adaptability of charging and discharging of the electric vehicle are improved, the charging cost is reduced, and the method is suitable for popularization and application. And the charging efficiency is improved, and popularization and promotion of the electric automobile are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging and discharging control, and in particular to an electric vehicle charging and discharging control method and system. Background Art

[0002] With the rapid development of the electric vehicle industry, the charging and discharging control technology of electric vehicles has become a research hotspot. At present, researchers have proposed a variety of charging and discharging scheduling models to improve the discharge capacity of electric vehicles and reduce charging costs, thereby optimizing the operating efficiency of the power system and the user experience. However, these models and methods still have certain limitations in practical applications.

[0003] The existing electric vehicle charging and discharging control technology still has many shortcomings in practical applications. Most of the existing scheduling models focus on single-objective optimization, only focusing on improving discharge capacity or reducing charging costs, and fail to comprehensively consider the relationship between discharge capacity and charging costs, and build a comprehensive first discharge scheduling model and second discharge scheduling model. The existing related technologies have limitations in optimization analysis. Most studies have failed to conduct in-depth analysis of the charging and discharging characteristics of different electric vehicles, resulting in a lack of pertinence in the formulated charging and discharging strategies, which cannot meet the needs of different users and cannot give full play to the regulatory role of electric vehicles in the power system. The existing optimization methods are often complex in calculation and difficult to implement in actual operation. The existing technology cannot respond quickly in the process of charging, which limits its effectiveness in practical applications. The existing technology has deficiencies in the formulation of charging modes. Most studies have failed to fully consider the diversity of user demand scenarios, resulting in a disconnect between the charging mode and the actual needs of users. The existing technology often lacks continuous optimization of the simulation results of the charging and discharging strategy, making it difficult for the formulated strategy to adapt to the ever-changing electricity market and user behavior. In practical applications, the existing technology generally has the problems of weak adaptability of the dispatching strategy, difficulty in coping with fluctuations in the grid load, lack of effective user demand prediction methods, low accuracy of the charging and discharging strategy, and ignoring the safety issues in the charging and discharging process of electric vehicles, which may cause potential risks to the grid and the electric vehicles themselves. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing electric vehicle charging and discharging control method has the problem that the scheduling model is not comprehensive, cannot adapt to the diverse user needs, and cannot optimize the charging and discharging strategy according to the simulation results, as well as how to achieve efficient and economical electric vehicle charging and discharging control.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for controlling charging and discharging of an electric vehicle, comprising constructing a first discharge scheduling model and a second discharge scheduling model according to the discharge capacity and the charging cost; performing optimization analysis on the first charging and discharging model and the second charging and discharging model to formulate different charging and discharging strategies for electric vehicles; formulating a charging mode according to the charging and discharging strategy and user demand scenarios, and optimizing the charging and discharging strategy according to the simulation results.

[0007] As a preferred solution of the electric vehicle charging and discharging control method of the present invention, the first discharge scheduling model includes a first objective function and a first constraint condition, and the first objective function F1 is established based on the balance of power supply and demand and the electric vehicle charging and discharging deviation, which is expressed as:

[0008]

[0009] Among them, ω1 is the target weight coefficient, which is used to adjust the penalty of system power balance, T is the total number of steps in the time period, t is the current time step, ψ(t) is the power matching error weighting factor, P d，t is the power demand at time t, P n，t is the charging power of n electric vehicles at time t, N0 is the total number of electric vehicles, P DE is the power supply of distributed energy at time t, P a is the reference power, PD n，t is the scheduled charging power of n electric vehicles at time t, α is the weight coefficient related to the charge and discharge power deviation, δ is the weight coefficient related to the load variance penalty, Var(P d，t ) is the power demand P d，t The constraints include charging capacity balance constraint, agent scheduling constraint in each time period, power supply and demand balance constraint, electric vehicle charging power constraint and electric vehicle discharging power constraint.

[0010] As a preferred solution of the electric vehicle charging and discharging control method of the present invention, the second discharge scheduling model includes a second objective function and a second constraint condition, and the second objective function F2 is constructed with the minimum charging cost as the goal, which is expressed as:

[0011]

[0012] in, is the average responsiveness within the region i governed by the agent, α t is the charging weighted coefficient at time t, β t is the discharge weighting coefficient at time t, T0 is the start time of charge and discharge, T1 is the end time of charge and discharge, P n，i，t is the charging power of n electric vehicles in the area i under the agency’s jurisdiction at time t, C c，i，tis the charging electricity price at time t in the area i under the agency’s jurisdiction, PR n，i，t is the discharge power of n electric vehicles in the area i under the agency’s jurisdiction at time t, C d，i，t is the discharge price at time t in the area i under the jurisdiction of the agent, λ is the balancing factor used to adjust the weight of the power fluctuation penalty, ΔP i，t is the power change at time t in the area i under the jurisdiction of the agent. The second constraint includes the battery capacity constraint and the total load constraint.

[0013] As a preferred solution of the electric vehicle charging and discharging control method described in the present invention, the optimization analysis includes generating multiple initial populations through a Latin hypercube sampling strategy, and calculating the fitness of each individual in the initial population under the first objective function and the second objective function.

[0014] As a preferred solution of the electric vehicle charging and discharging control method of the present invention, the optimization analysis also includes updating the charging power P of n electric vehicles at time t by simulating the rolling behavior of dung beetles. n，t , the discharge power PR of n electric vehicles at time t n，t , the charging electricity price C at time t in the area i under the agency’s jurisdiction c，i，t and the discharge price C at time t in the area i under the agency’s jurisdiction d,i，t , simulating the reproduction behavior of dung beetles to update the power supply P of distributed energy at time t DE , by simulating foraging behavior, the power matching error weighting factor ψ(t) is optimized and the power change ΔP at time t in the area i governed by the agent is i，t , randomly adjust the position of individual parameters through sine and cosine functions, use the difference vector to mutate the updated individual, and judge whether the maximum number of iterations is reached. If so, end it, otherwise recalculate the fitness.

[0015] As a preferred solution of the electric vehicle charging and discharging control method described in the present invention, the charging mode includes a peak-valley electricity price difference charging mode, an intelligent scheduling mode and a balanced charging mode.

[0016] As a preferred solution of the electric vehicle charging and discharging control method described in the present invention, the simulation includes simulating the charging and discharging behaviors of electric vehicles under different charging modes, different electricity price fluctuations, different load demands, and different battery conditions.

[0017] Another object of the present invention is to provide an electric vehicle charging and discharging control system, which can formulate different electric vehicle charging and discharging strategies by performing optimization analysis on a first charging and discharging model and a second charging and discharging model, thereby solving the problems of poor grid operation stability and low economic benefits contained in current electric vehicle charging and discharging control technology.

[0018] As a preferred solution of the electric vehicle charging and discharging control system described in the present invention, it includes: a charging and discharging scheduling module, an optimization calculation module, and a strategy adjustment module; the charging and discharging scheduling module is used to construct a first discharge scheduling model and a second discharge scheduling model according to the discharge capacity and the charging cost; the optimization calculation module is used to perform optimization analysis on the first charging and discharging model and the second charging and discharging model, and formulate charging and discharging strategies for electric vehicles under different scenarios; the strategy adjustment module is used to formulate a charging mode according to the charging and discharging strategy and user demand scenarios, and optimize the charging and discharging strategy according to the simulation results.

[0019] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for controlling charging and discharging of an electric vehicle.

[0020] A computer-readable storage medium stores a computer program, which implements the steps of an electric vehicle charging and discharging control method when executed by a processor.

[0021] Beneficial effects of the present invention: The electric vehicle charging and discharging control method provided by the present invention realizes the refined management of the charging and discharging behavior of the electric vehicle by constructing the first discharge scheduling model and the second discharge scheduling model, adjusts the penalty of the system power balance, thereby better meeting the balance of power supply and demand, improving the stability of the power grid operation, ensuring the reasonable allocation of electric vehicles in the charging and discharging process, reducing the charging cost, and improving the charging efficiency. By performing optimization analysis on the first charging and discharging model and the second charging and discharging model, and formulating different electric vehicle charging and discharging strategies, the diversity and globality of the optimization process are ensured, and the possibility of finding the optimal solution is improved. By calculating the fitness, the charging and discharging strategy that better meets the actual needs can be screened out, and the overall performance of the electric vehicle charging and discharging is improved. By simulating the charging and discharging behavior of electric vehicles under different charging modes, different electricity price fluctuations, different load demands, and different battery conditions, the actual effect of each strategy can be more accurately evaluated, thereby optimizing the charging and discharging strategy, reducing operating costs, and improving charging efficiency. The present invention has achieved better results in improving charging efficiency, enhancing the stability and economy of power grid operation, and improving the overall performance of electric vehicle charging and discharging. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1An overall flow chart of an electric vehicle charging and discharging control method provided for the first embodiment of the present invention.

[0024] Figure 2 An overall flow chart of an electric vehicle charging and discharging control method and system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] Example 1, reference Figure 1 , as an embodiment of the present invention, provides an electric vehicle charging and discharging control method, comprising:

[0027] S1: Construct a first discharge scheduling model and a second discharge scheduling model according to the discharge capacity and the charging cost.

[0028] Furthermore, by establishing a first charging and discharging scheduling model to optimize the charging load and discharging output power in each time period, the power supply and demand balance in each time period is ensured, while reducing the power matching error of the electric vehicle during the charging and discharging process; the first discharging scheduling model includes a first objective function and a first constraint condition, and the first objective function F1 is established based on the power supply and demand balance and the electric vehicle charging and discharging deviation, which is expressed as:

[0029]

[0030] Among them, ω1 is the target weight coefficient, which is used to adjust the penalty of system power balance, T is the total number of steps in the time period, t is the current time step, ψ(t) is the power matching error weighting factor, P d，t is the power demand at time t, P n，t is the charging power of n electric vehicles at time t, N0 is the total number of electric vehicles, P DE is the power supply of distributed energy at time t, P a is the reference power, PD n，t is the scheduled charging power of n electric vehicles at time t, α is the weight coefficient related to the charge and discharge power deviation, δ is the weight coefficient related to the load variance penalty, Var(P d，t ) is the power demand P d，t The constraints include charging capacity balance constraint, agent scheduling constraint in each time period, power supply and demand balance constraint, electric vehicle charging power constraint and electric vehicle discharging power constraint.

[0031] It should be noted that the second charging and discharging scheduling model is constructed based on the average response capability and charging cost of the agent's jurisdiction area, which reduces the user's charging cost while enhancing the user's response capability to grid scheduling and minimizing the impact of power fluctuations on the grid; the second discharge scheduling model includes a second objective function and a second constraint condition, and the second objective function F2 is constructed with the minimum charging cost as the goal, which is expressed as:

[0032]

[0033] in, is the average responsiveness within the region i governed by the agent, α t is the charging weighted coefficient at time t, β t is the discharge weighting coefficient at time t, r0 is the start time of charge and discharge, T1 is the end time of charge and discharge, Pn ，i，t is the charging power of n electric vehicles in the area i under the agency’s jurisdiction at time t, C c，i，t is the charging electricity price at time t in the area i under the agency’s jurisdiction, PR n，i，t is the discharge power of n electric vehicles in the area i under the agency’s jurisdiction at time t, C d，i，t is the discharge price at time t in the area i under the jurisdiction of the agent, λ is the balancing factor used to adjust the weight of the power fluctuation penalty, ΔP i，t is the power change at time t in the area i under the agency’s jurisdiction. The second constraint includes the battery capacity constraint and the total load constraint; Used to minimize the charging cost of the agent in the area i under its jurisdiction.

[0034] It should also be noted that To ensure the power demand and power supply in each time period (the charging power of n electric vehicles at time t, the charging power P n，t , Distributed energy supply P DE and reference power P a ), preferably, by using the quadratic variance form, a larger deviation in power balance will be punished more severely, thereby strengthening the supply and demand balance constraint of the power system. The charging power P used to punish the charging power of n electric vehicles at time t n，t and the predetermined charging power PD n，t The absolute value form is used to prevent small deviations from being overly punished, thereby allowing electric vehicles to be flexibly dispatched within a certain range, which is more in line with the needs of actual charging and discharging scenarios. d，t ) is used to minimize the power demand P d，tThe variance of the power demand is smoother, and the pressure and instability of the power grid caused by demand fluctuations are reduced. Preferably, by introducing the variance penalty term, the power demand curve can be smoother, and the overall stability of the power system and the feasibility of the dispatch plan can be improved. In order to ensure the feasibility of the electric vehicle charging and discharging process, the corresponding constraints are established. The constraints include charging capacity balance constraints, agent dispatch constraints in each period, power supply and demand balance constraints, electric vehicle charging power constraints, and electric vehicle discharge power constraints. In order to ensure the travel needs of users and ensure that the total load remains unchanged during the dispatch time period, the charging capacity balance constraint is defined, which is expressed as:

[0035]

[0036] Among them, PR n，t is the discharge power of n electric vehicles at time t; the dispatch constraints of the agent in each period are defined as:

[0037]

[0038] in, is the average charging power of n electric vehicles, is the average discharge power of n electric vehicles, A represents the connection status between electric vehicles and charging piles in the area, A=0 or 1; represents the average response capacity of electric vehicles in the area i under the jurisdiction of the agent, and the power supply and demand balance constraint ensures that in each time period t, the total load of the system (including the charging and discharging power of electric vehicles) is balanced with the power supply, which is expressed as:

[0039]

[0040] Define the electric vehicle charging power constraint to constrain the maximum charging power of each electric vehicle in each time period, expressed as:

[0041] 0≤P n，t ≤PR n，max

[0042] The electric vehicle discharge power constraint is defined to constrain the maximum discharge power of each electric vehicle in each time period, which is expressed as:

[0043] 0≤PR n，t ≤P n，max

[0044] Among them, P n，max is the maximum charging power of n electric vehicles, PR n，max is the maximum discharge power of n electric vehicles.

[0045] It should also be noted that by constructing the first discharge scheduling model and the second discharge scheduling model, refined management of electric vehicle charging and discharging behaviors is achieved, and the penalty for system power balance is adjusted, so as to better meet the balance of power supply and demand, improve the stability of power grid operation, ensure the reasonable allocation of electric vehicles in the charging and discharging process, reduce charging costs, and improve charging efficiency.

[0046] S2: Optimizing and analyzing the first charging and discharging model and the second charging and discharging model to formulate different charging and discharging strategies for electric vehicles.

[0047] Furthermore, the optimization analysis includes generating multiple initial populations through a Latin hypercube sampling strategy, and calculating the fitness of each individual in the initial population under the first objective function and the second objective function.

[0048] It should be noted that the charging power P of n electric vehicles at time t is updated by simulating the rolling behavior of dung beetles. n，t , the discharge power PR of n electric vehicles at time t n，t , the charging electricity price C at time t in the area i under the agency’s jurisdiction c，i，t and the discharge price C at time t in the area i under the agency’s jurisdiction d,i，t , simulating the reproduction behavior of dung beetles to update the power supply P of distributed energy at time t DE , by simulating foraging behavior, the power matching error weighting factor ψ(t) is optimized and the power change ΔP at time t in the area i governed by the agent is i，t , randomly adjust the position of individual parameters through sine and cosine functions, use the difference vector to mutate the updated individual, and judge whether the maximum number of iterations is reached. If so, end it, otherwise recalculate the fitness.

[0049] It should also be noted that the optimal charging and discharging strategy suitable for different situations is found based on a multi-population genetic optimization algorithm. The algorithm can find the balance point between the lowest user cost and the highest dispatch response capability under the premise of meeting the requirements of grid stability. The specific steps include step (1) generating multiple initial populations through a Latin hypercube sampling strategy, and the individuals in each population represent the charging and discharging strategies under different situations to ensure uniform distribution in each parameter space; step (2) calculating the fitness of each individual in the initial population under the first objective function and the second objective function; for each individual, calculating the objective function value F1 based on the balance of power supply and demand and the charging and discharging deviation of electric vehicles. For example, the calculation result of the charging and discharging strategy of an individual in the period from t = 1 to t = T is expressed as:

[0050]

[0051] Similarly, the objective function value F2 of each individual based on minimizing the charging cost is calculated as follows:

[0052]

[0053] Furthermore, the fitness value is set as the weighted sum of the two, expressed as:

[0054] Fitness=q1·F1+q2·F2

[0055] Wherein, q1 and q2 are preset weights, and the value range is (0, 1); Step (3) updates the charging power P of n electric vehicles at time t by simulating the rolling behavior of dung beetles n，t , the discharge power PR of n electric vehicles at time t n，t , the charging electricity price C at time t in the area i under the agency’s jurisdiction c，i，t and the discharge price C at time t in the area i under the agency’s jurisdiction d,i，t ; For example, update the charging power P of n vehicles at time t n，t , expressed as:

[0056]

[0057] Update the discharge power PR of n vehicles at time t n，t , expressed as:

[0058]

[0059] in, is the updated charging power of n vehicles at time t, is the charging power of n vehicles at time t before updating, is the updated discharge power of n vehicles at time t, is the discharge power of n vehicles at time t before the update, r is the random factor, μ is the adjustment coefficient, and at the same time, the charging electricity price C in the area under the jurisdiction of the agent is updated c，i，t and discharge price C d，i，t , expressed as:

[0060]

[0061] in, The updated charging electricity price within the agent's jurisdiction area. The charging electricity price in the agent's jurisdiction before the update. is the updated discharge electricity price within the agent’s jurisdiction. is the discharge electricity price in the agent's jurisdiction before the update, and ΔC is the price adjustment range; Step (4) updates the power supply P of the distributed energy at time t by simulating the reproduction behavior of dung beetles DE , expressed as:

[0062]

[0063] in, is the power supply of distributed energy at time t after adjustment P DE , load is the total demand of the power system, supply is the total supply capacity of the power system, and τ is the adjustment factor; Step (5) optimizes the power matching error weighting factor ψ(t) by simulating foraging behavior and is the power change ΔP at time t in the area i under the jurisdiction of the agent i，t , expressed as:

[0064] ψ(t) new =ψ(t) old +φ·random(-1,1)

[0065]

[0066] Where ψ(t) new is the updated power matching error weighting factor, ψ(t) old is the power matching error weighting factor before updating, φ is the learning rate, is the power change at time t in area i under the jurisdiction of the updated agent, is the power change at time t in the area i under the jurisdiction of the agent before the update, and ∈ is the preset variation range value; Step (6) randomly adjusts the position of the individual parameters through the sine and cosine functions. In order to increase the exploration depth of the search space, the sine and cosine functions are introduced to randomly adjust the position of the individual parameters. By imitating the raiding behavior of the stealing dung beetle when capturing food, the randomness and jump of the exploration are increased, which helps the individual jump out of the local optimum and explore a wider search space. The position update formula is:

[0067] xi′=xi+α·cos(β·t)

[0068] Among them, xi′ is the position of the individual after parameter adjustment, xi is the position of the individual before parameter adjustment, α and β are parameters that control the update amplitude, and t is the time step; step (7) uses the difference vector to perform mutation operation on the updated individual to increase the diversity of the population. For example, three different individuals xa, xb, xc are randomly selected from the current population to generate a mutation vector vi, which is expressed as:

[0069] vi=xa+F·(xb-xc)

[0070] Wherein, F is a scaling factor, and its value range is [0, 2]. Step (8) determines whether the maximum number of iterations has been reached. If so, the process ends; otherwise, the process returns to step (2).

[0071] It should also be noted that by performing optimization analysis on the first charging and discharging model and the second charging and discharging model and formulating different electric vehicle charging and discharging strategies, the diversity and globality of the optimization process are ensured, the possibility of finding the optimal solution is improved, and by calculating the fitness, the charging and discharging strategy that better meets the actual needs can be screened out, thereby improving the overall performance of electric vehicle charging and discharging.

[0072] S3: Formulate charging mode according to charging and discharging strategy and user demand scenarios, and optimize charging and discharging strategy according to simulation results.

[0073] Furthermore, based on the analysis results of the optimization analysis, key characteristics are extracted, including charging period selection (the priority period for charging in the optimization strategy), charging power adjustment suggestions, discharge suggestions, user demand response, and grid response. A variety of charging modes are formulated based on the extracted key characteristics and user demand scenarios. User demand scenarios include daily commuting (users usually use vehicles within a fixed time period), economy priority (users give priority to reducing charging costs), and environmental protection priority (users hope to use renewable energy for charging as much as possible); charging modes include peak-valley electricity price difference charging mode, intelligent scheduling mode, and balanced charging mode.

[0074] It should be noted that various charging modes are used to simulate the charging and discharging behaviors of electric vehicles under different electricity price fluctuations, different load demands, and different battery conditions; peak-valley electricity price difference charging mode: high-power charging during the low electricity price period (such as 0:00-6:00); discharging during the peak electricity price period (such as 17:00-21:00); intelligent scheduling mode: dynamically adjust the charging and discharging power based on real-time electricity prices and load forecasts, and the charging strategy changes dynamically according to the optimization results; balanced charging mode: maintain a uniform charging rate throughout the day to avoid load fluctuations during peak and valley periods; further, simulate the large fluctuations in electricity prices within a day, test the three modes, and observe the impact of the charging and discharging strategies of electric vehicles under each mode on the cost; Scenario 1: Electricity price fluctuations, simulate the large fluctuations in electricity prices within a day, and test the three charging modes Tests were conducted to observe the impact of the charging and discharging strategies of electric vehicles under various modes on costs; Scenario 2: Load demand changes, simulating the fluctuations in load demand in different time periods, and analyzing the responsiveness of each mode under load demand fluctuations; Scenario 3: Battery status, simulating the impact of three charging modes on battery life and operating costs under different initial battery charging states, and further optimizing the charging and discharging strategies based on the simulation results. For example, under Scenario 1, the cost of the intelligent dispatching mode is the lowest, and the system load stability of the balanced charging mode is the best. The charging and discharging strategies are further optimized to combine the intelligent dispatching mode with the peak-valley electricity price difference mode to minimize load fluctuations while achieving low costs. The simulation includes simulating the charging and discharging behaviors of electric vehicles under different charging modes, different electricity price fluctuations, different load demands, and different battery states.

[0075] It should also be noted that charging modes are formulated according to charging and discharging strategies and user demand scenarios, and charging and discharging strategies are optimized according to simulation results to meet different user needs and improve the adaptability of charging modes. By simulating the charging and discharging behaviors of electric vehicles under different charging modes under different electricity price fluctuations, different load demands, and different battery conditions, the actual effects of each strategy can be evaluated more accurately, thereby optimizing the charging and discharging strategies, reducing operating costs, and improving charging efficiency.

[0076] Embodiment 2 is an embodiment of the present invention, which provides an electric vehicle charging and discharging control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0077] Firstly, according to the discharge capacity and charging cost, two discharge scheduling models are constructed: the first discharge scheduling model and the second discharge scheduling model. The first model mainly considers the balance of power supply and demand and the charge and discharge deviation of electric vehicles, while the second model aims to minimize the charging cost. In the process of constructing the model, special attention is paid to the setting of key parameters such as the target weight coefficient, the power matching error weighting factor, and the weight coefficient related to the load variance penalty; the two models are optimized, and the Latin hypercube sampling strategy is used to generate multiple initial populations, and the fitness of each population under the first and second objective functions is calculated. By simulating the rolling behavior, reproduction behavior and foraging behavior of dung beetles, the fitness of the population is updated. The charging and discharging power, charging electricity price and discharging electricity price of electric vehicles, as well as the power supply of distributed energy, more effectively optimize the power matching error weighting factor and the power change. Finally, according to the simulation results and user demand scenarios, charging modes including peak and valley electricity price difference charging mode, intelligent scheduling mode and balanced charging mode are formulated. This process involves the simulation of the charging and discharging behavior of electric vehicles under different charging modes, different electricity price fluctuations, different load demands and different battery conditions. In summary, the electric vehicle charging and discharging control method proposed in the present invention is innovative and advantageous compared with the prior art in terms of improving efficiency, reducing costs, optimizing power allocation and improving user satisfaction.

[0078] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides an electric vehicle charging and discharging control system, including a charging and discharging scheduling module, an optimization calculation module, and a strategy adjustment module.

[0079] The charge and discharge scheduling module is used to construct the first discharge scheduling model and the second discharge scheduling model according to the discharge capacity and the charging cost; the optimization calculation module is used to perform optimization analysis on the first charge and discharge model and the second charge and discharge model, and formulate the charge and discharge strategies of electric vehicles under different scenarios; the strategy adjustment module is used to formulate the charging mode according to the charge and discharge strategy and user demand scenarios, and optimize the charge and discharge strategy according to the simulation results.

[0080] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0082] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for controlling charging and discharging of an electric vehicle, characterized in that: include: Constructing a first discharge scheduling model and a second discharge scheduling model according to the discharge capacity and the charging cost; Optimizing the first charging and discharging model and the second charging and discharging model to formulate different charging and discharging strategies for electric vehicles; Formulate charging modes according to charging and discharging strategies and user demand scenarios, and optimize charging and discharging strategies based on simulation results.

2. The electric vehicle charging and discharging control method according to claim 1, characterized in that: The first discharge scheduling model includes a first objective function and a first constraint condition. The first objective function F1 is established based on the balance of power supply and demand and the charge and discharge deviation of electric vehicles, and is expressed as: Among them, ω1 is the target weight coefficient, which is used to adjust the penalty of system power balance, T is the total number of steps in the time period, t is the current time step, ψ(t) is the power matching error weighting factor, P d,t is the power demand at time t, P n,t is the charging power of n electric vehicles at time t, N0 is the total number of electric vehicles, P DE is the power supply of distributed energy at time t, P a is the reference power, PD n,t is the scheduled charging power of n electric vehicles at time t, α is the weight coefficient related to the charge and discharge power deviation, δ is the weight coefficient related to the load variance penalty, Var(P d,t ) is the power demand P d,t The constraints include charging capacity balance constraint, agent scheduling constraint in each time period, power supply and demand balance constraint, electric vehicle charging power constraint and electric vehicle discharging power constraint.

3. The electric vehicle charging and discharging control method according to claim 2, characterized in that: The second discharge scheduling model includes a second objective function and a second constraint condition. The second objective function F2 is constructed with the minimum charging cost as the goal, which is expressed as: in, is the average responsiveness within the region i governed by the agent, α t is the charging weighted coefficient at time t, β t is the discharge weighting coefficient at time t, T0 is the start time of charge and discharge, T1 is the end time of charge and discharge, P n,i,t is the charging power of n electric vehicles in the area i under the agency’s jurisdiction at time t, C c,i,t is the charging electricity price at time t in the area i under the agency’s jurisdiction, PR n,i,t is the discharge power of n electric vehicles in the area i under the agency’s jurisdiction at time t, C d,i,t is the discharge price at time t in the area i under the jurisdiction of the agent, λ is the balancing factor used to adjust the weight of the power fluctuation penalty, ΔP i,t is the power change at time t in the area i under the jurisdiction of the agent. The second constraint includes the battery capacity constraint and the total load constraint.

4. The electric vehicle charging and discharging control method according to claim 3, characterized in that: The optimization analysis includes generating multiple initial populations through a Latin hypercube sampling strategy, and calculating the fitness of each individual in the initial population under a first objective function and a second objective function.

5. The electric vehicle charging and discharging control method according to claim 4, characterized in that: The optimization analysis also includes updating the charging power P of n electric vehicles at time t by simulating the rolling behavior of dung beetles n,t , the discharge power PR of n electric vehicles at time t n,t , the charging electricity price C at time t in the area i under the agency’s jurisdiction c,i,t and the discharge price C at time t in the area i under the agency’s jurisdiction d,i,t , simulating the reproduction behavior of dung beetles to update the power supply P of distributed energy at time t DE , by simulating foraging behavior, the power matching error weighting factor ψ(t) is optimized and the power change ΔP at time t in the area i governed by the agent is i,t , randomly adjust the position of individual parameters through sine and cosine functions, use the difference vector to mutate the updated individual, and judge whether the maximum number of iterations is reached. If so, end it, otherwise recalculate the fitness.

6. The electric vehicle charging and discharging control method according to claim 5, characterized in that: The charging modes include peak-valley electricity price difference charging mode, intelligent scheduling mode and balanced charging mode.

7. The electric vehicle charging and discharging control method according to claim 6, characterized in that: The simulation includes simulating the charging and discharging behaviors of electric vehicles under different charging modes, different electricity price fluctuations, different load demands, and different battery states.

8. A system using the electric vehicle charging and discharging control method according to any one of claims 1 to 7, characterized in that: Including charging and discharging scheduling module, optimization calculation module, and strategy adjustment module; The charging and discharging scheduling module is used to construct a first discharging scheduling model and a second discharging scheduling model according to the discharging capacity and the charging cost; The optimization calculation module is used to perform optimization analysis on the first charging and discharging model and the second charging and discharging model, and formulate charging and discharging strategies for electric vehicles under different scenarios; The strategy adjustment module is used to formulate a charging mode according to the charging and discharging strategy and user demand scenarios, and optimize the charging and discharging strategy according to the simulation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electric vehicle charging and discharging control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electric vehicle charging and discharging control method according to any one of claims 1 to 7 are implemented.

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