Electric vehicle battery charging and discharging method for peak load shifting and related device

Abstract

The invention discloses an electric vehicle battery charging and discharging method for peak load shifting and a related device, and belongs to the technical field of electric vehicle charging and discharging. The method comprises the following steps: constructing a battery EQU life model, and calculating to obtain the daily energy storage loss cost of the battery; based on the daily energy storage loss cost of the battery, considering the comprehensive load and battery degradation cost of the electric vehicle, the peak-valley difference of the comprehensive load and the peak-valley fluctuation of the comprehensive load to establish a target function; establishing a power battery charging and discharging constraint, a power battery energy continuity constraint and a power battery EQU life model constraint; a peak clipping and valley filling multi-objective optimization model is constructed and obtained; and solving the multi-objective optimization model to obtain a battery charging and discharging strategy. The influence of charging and discharging at different depths on the service life of the battery is comprehensively considered, and more accurate service life management is realized. Through a three-objective optimization strategy of comprehensively considering the battery life cost, the load balance and the peak-valley difference, the system is ensured to realize the balance of economical efficiency and reliability while peak clipping and valley filling are performed.

Description

Technical Field

[0001] The invention belongs to the technical field of electric vehicle charging and discharging, and relates to an electric vehicle battery charging and discharging method and related device for peak shaving and valley filling. Background Art

[0002] With the rapid development of renewable energy and the continuous growth of electricity demand, electric vehicles (EVs) have been widely used in the fields of transportation and energy as a green and efficient means of transportation. Electric vehicles can not only alleviate the energy crisis, but also participate in the load regulation of the power grid as mobile energy storage devices. Peak shaving and valley filling is an important strategy for power grid optimization. By discharging during peak power consumption and charging during valley periods, the load curve can be smoothed and the operation efficiency of the power grid can be improved. However, the large-scale access and charging and discharging behavior of electric vehicles have put forward higher requirements on the stability of the power grid. How to reasonably dispatch the charging and discharging of electric vehicles has become a hot topic in current research.

[0003] Existing technologies have made some progress in the orderly charging and discharging strategies of electric vehicles. For example, charging is optimized through new energy probability models and peak-valley electricity price models, or power fluctuations of new energy power stations are scheduled based on dynamic models. However, these studies usually ignore the battery degradation cost, or only simulate the simple loss mechanism of electric vehicle batteries, and fail to fully consider the complexity of battery life. In addition, although some methods discuss the relationship between battery life and discharge depth, they do not optimize the precise calculation of the equivalent full cycle number of batteries, resulting in low accuracy in battery loss prediction and inability to effectively balance the contradiction between battery life and grid demand.

[0004] The life of electric vehicle power batteries is determined by both floating charge life and cycle life. Frequent deep discharge will significantly shorten the battery cycle life and increase the cost of use. However, most existing peak-shaving and valley-filling scheduling strategies fail to fully consider the constraints of battery life, only focusing on short-term economic efficiency and ignoring long-term cost control and maximum battery utilization. In view of these shortcomings, a multi-objective optimization method that comprehensively considers the number of equivalent full cycles of batteries, charge and discharge depth, and load balance is urgently needed to better improve the load balancing capacity of the power grid, while extending battery life and reducing usage costs. Summary of the invention

[0005] The purpose of the present invention is to provide an electric vehicle battery charging and discharging method and related devices for peak shaving and valley filling, so as to solve the technical problems of the existing peak shaving and valley filling scheduling strategy, such as poor grid load balancing ability, poor long-term economy and affecting battery life.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for charging and discharging a battery of an electric vehicle for peak shaving and valley filling, comprising the following steps:

[0008] Construct a battery EQU life model and calculate the daily energy storage depreciation cost of the battery;

[0009] Based on the daily energy storage depreciation cost of the battery, the objective function is established by considering the comprehensive load of electric vehicles and the battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation; the power battery charging and discharging constraints, the power battery energy continuity constraints and the power battery EQU life model constraints are established; and a multi-objective optimization model for peak shaving and valley filling is constructed;

[0010] The peak shaving and valley filling multi-objective optimization model is solved to obtain the battery charging and discharging strategy.

[0011] Furthermore, the steps of constructing a battery EQU life model and calculating the daily energy storage depreciation cost of the battery specifically include:

[0012] Obtain the battery investment cost, operation and maintenance cost, and battery calendar life, and calculate the annual average cost C APV , the specific calculation formula is:

[0013]

[0014] In the formula, C op is the battery investment cost; C PV is the operation and maintenance cost; T calendar is the battery calendar life; r is the return on capital;

[0015] Based on the average annual cost C APV Calculate the daily energy storage loss cost of the battery C bat ;

[0016] Calculate the equivalent full cycle number of the battery N eq,i , the specific calculation formula is:

[0017]

[0018] Where, d i is the discharge depth of the i-th single charge and discharge cycle; C is the battery loss coefficient;

[0019] The equivalent full cycle number of the power battery of electric vehicles is limited. The specific expression is:

[0020]

[0021] Where, I is the number of EV daily charging times; and It is based on the data provided by the battery manufacturer; It means ensuring that the battery can work normally until the maximum daily equivalent cycle number corresponding to the float charge life is reached.

[0022] Furthermore, the step of establishing the objective function based on the daily energy storage depreciation cost of the battery, taking into account the comprehensive load of the electric vehicle and the battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation, specifically includes:

[0023] Considering the comprehensive load of electric vehicles and the battery degradation cost, the objective function F1 is established. The specific calculation formula is:

[0024] F1=C1+C bat

[0025] Where C1 is the comprehensive load cost, C bat The daily energy storage cost of the battery;

[0026] Considering the comprehensive load peak-to-valley difference of peak shaving and valley filling, the objective function F2 is established. The specific calculation formula is:

[0027] F2=min[max(P t )-min(P t )]

[0028]

[0029] Where P t base is the original load, p i,t The charging and discharging power of the electric vehicle at each moment, charging is positive;

[0030] Considering the peak-valley fluctuation of the comprehensive load of peak shaving and valley filling, the objective function F3 is established. The specific calculation formula is:

[0031]

[0032] By assigning weights to the multi-objective problem, a single-objective optimization process is performed, and the following is obtained:

[0033]

[0034] F 2M =max(P t base )-min(P t base )

[0035]

[0036] Where P t base is the original load; w1, w2 and w3 are weights.

[0037] Furthermore, the expression of the power battery charge and discharge constraint is:

[0038]

[0039] Where, N represents the number of electric vehicles; V2G Indicates the number of cars participating in the V2G protocol; Indicates the time when the car is connected to the power grid; Indicates the time when the car leaves the power grid; P dis is the maximum discharge power; P char is the maximum charging power.

[0040] Furthermore, the process of establishing the energy continuity constraint of the power battery specifically includes:

[0041] Constraints are set for the charging and discharging process of each electric vehicle. The relationship between the dynamic charging process of the electric vehicle and the SOC is:

[0042]

[0043] In the formula, p i,t is the charge and discharge power in the current period, η is the charge and discharge efficiency; E i Indicates the current total power of the electric vehicle; SOC i Indicates the current state of charge;

[0044] Randomize the initial power of each EV when it is connected Expected power Set to 75% of the total power of the power battery; the sum of the initial power of each vehicle today and the total charge is not less than the expected power. The specific expression is:

[0045]

[0046] Considering the safety and stability of electric vehicles, the SOC of each period is limited to the available range. The specific expression is:

[0047]

[0048] In the formula, Indicates the initial power of each EV when it is connected; SOC i,min Indicates the minimum charge; SOC i,max Indicates maximum power.

[0049] Furthermore, the process of establishing the power battery EQU life model constraint specifically includes:

[0050] Determine the battery discharge depth at any time. The segment the machine is in, the battery discharge depth at time t The specific calculation formula is as follows:

[0051]

[0052] In the formula, Indicates the discharge depth of the battery at time t; Indicates the SOC of the battery at time t;

[0053] Since the parameters are piecewise linearized for the discharge depth, the constraint condition is used to obtain the segment where the battery energy storage discharge depth is located. The expression of the constraint condition is:

[0054]

[0055] In the formula, is the lower limit of the discharge depth of the dth segment; is the upper limit of the discharge depth of the dth segment;

[0056] Find the moment when the energy storage charge and discharge state changes:

[0057]

[0058] In the formula, is the charge and discharge status at time t, 1 is charging and 0 is discharging; is a 0-1 variable. If the energy storage starts charging at time t, is 1;

[0059] Determine the number of daily discharges:

[0060]

[0061] Through the battery EQU life model, the total equivalent cycle number in one day is limited, specifically:

[0062]

[0063] In the formula, and Based on the data provided by the battery manufacturer; t-1 is the discharge depth of the t-1th single charge and discharge cycle.

[0064] Furthermore, the step of solving the peak shaving and valley filling multi-objective optimization model to obtain a battery charging and discharging strategy specifically includes: obtaining the charging and discharging time and corresponding power of a large number of electric vehicles by solving the peak shaving and valley filling multi-objective optimization model, thereby regulating the charging and discharging of electric vehicle batteries.

[0065] In a second aspect, the present invention provides an electric vehicle battery charging and discharging system for peak load shaving and valley load filling, comprising:

[0066] The battery life model building module is used to build the battery EQU life model and calculate the daily energy storage depreciation cost of the battery;

[0067] The multi-objective optimization model building module is used to establish the objective function based on the daily energy storage depreciation cost of the battery, taking into account the comprehensive load of electric vehicles and battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation; establish the power battery charging and discharging constraints, the power battery energy continuity constraints and the power battery EQU life model constraints; and construct a multi-objective optimization model for peak shaving and valley filling;

[0068] The charging and discharging strategy solving module is used to solve the peak shaving and valley filling multi-objective optimization model to obtain the battery charging and discharging strategy.

[0069] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0070] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention discloses a method and related device for charging and discharging electric vehicle batteries for peak shaving and valley filling, introduces an equivalent full cycle number model (EQU), comprehensively considers the impact of different depths of charging and discharging on battery life, and realizes more accurate life management. By comprehensively considering the three-objective optimization strategy of battery life cost, load balance and peak-to-valley difference, it is ensured that the system achieves a balance between economy and reliability while shaving peaks and filling valleys. And the priority of different objectives is adjusted by using weight coefficients, so that the scheme can flexibly adapt to the needs of various scenarios. In addition, the present invention reasonably designs the charging and discharging plan of electric vehicles, discharges during peak power consumption and charges during valley periods, significantly reduces the peak-to-valley difference of load and smoothes load fluctuations. The optimization strategy reduces the dispatching cost and stability risk of the power grid caused by load fluctuations, and improves the economy and safety of power grid operation. The optimized charging and discharging strategy effectively reduces the degradation rate of the battery, makes the actual life of the power battery close to its floating charge life, and minimizes the economic losses caused by battery degradation. In addition, the present invention introduces the constraint of the equivalent full cycle number, combines the conditions such as the SOC range of electric vehicles, the charging and discharging power limit, and the random access and off-grid connection of vehicles, to ensure the feasibility of the optimization model in actual complex scenarios. Specific optimization constraints are designed according to the randomness and dynamics of the V2G (vehicle to grid) protocol, which enhances the robustness and wide applicability of the scheme.

[0073] Furthermore, the present invention adopts the mixed integer linear programming (MILP) method to accurately solve the charging and discharging plan, and the optimization effect is significant. The simulation results show that compared with the traditional model, the model proposed by the present invention can effectively reduce the battery life cost and the total operating cost, while improving the load characteristics of the power grid. In addition, the present invention reduces the cost of users using electric vehicle energy storage to participate in V2G, and improves the user's acceptance of electric vehicles participating in the peak-shaving and valley-filling mode of the power grid. By extending battery life and improving the efficiency of power grid operation, a solid foundation has been laid for the access and promotion of new energy, which helps the development of a low-carbon economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0075] Figure 1 is a flow chart of the method of the present invention;

[0076] Figure 2 is a schematic diagram of the system of the present invention;

[0077] Figure 3 This is a typical daily load power curve of an office building in an embodiment of the present invention;

[0078] FIG4 is a time-sharing charging power diagram of an electric vehicle according to an embodiment of the present invention. Figure 4a This is the result of the first experiment. Figure 4b The results of the second experiment are: Figure 4c The results of the third experiment are: Figure 4d This is the result of the fourth experiment;

[0079] Figure 5 The various loads and total load curves of the embodiment of the present invention;

[0080] Figure 6 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0081] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0082] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0083] See also Figure 1 The embodiment of the present invention discloses a method for charging and discharging a battery of an electric vehicle for peak shaving and valley filling, comprising the following steps:

[0084] S1, build the battery EQU life model and calculate the daily energy storage depreciation cost of the battery;

[0085] When evaluating the life of a battery energy storage system, the traditional method calculates the impact of shallow discharge cycles based on the maximum depth of discharge in a typical day. This method is based on the maximum daily depth of discharge and may overestimate the cost of energy storage. To address this problem, a model that considers the number of equivalent full cycles is introduced.

[0086] The core idea of ​​this model is to convert charge and discharge cycles of different depths into equivalent 100% discharge depth cycles. By limiting the number of equivalent full cycles per day, the cycle life of the battery energy storage system is ensured to be no less than the floating charge life. This ensures that electric vehicles can work normally within the predetermined life span, making the actual calendar life of the power battery equal to its floating charge life, maximizing battery utilization while reducing user costs and improving user willingness. The following steps are included:

[0087] Calculate the average annual cost C APV , C op is the battery investment cost, C PV is the operation and maintenance cost, T calendar is the battery calendar life, and r is the return on capital.

[0088]

[0089] Then the annual average cost C APV Calculate the daily energy storage depreciation cost C bat .

[0090] In addition, the equivalent full cycle number N of the battery is calculated according to the following formula: eq,i :

[0091]

[0092] Where, d i is the discharge depth of the ith single charge and discharge cycle. C exists to convert the battery loss of the electric vehicle during normal driving, and considering the manufacturer's design of vehicle kinetic energy recovery, it is approximately taken as 0.5.

[0093] Ultimately, the equivalent full cycle number of electric vehicle power batteries is limited to a certain range, namely:

[0094]

[0095] Where I is the number of times the EV is charged per day. The left side of the above inequality calculates the number of equivalent full cycles in a typical day, where and It is a parameter obtained by linearizing different discharge depth intervals based on the data provided by the battery manufacturer. The right side of the inequality indicates the maximum daily equivalent cycle number that ensures that the battery can work normally until the floating charge life is reached.

[0096] S2, based on the daily energy storage depreciation cost of the battery, consider the comprehensive load of electric vehicles and battery degradation cost, comprehensive load peak-to-valley difference and comprehensive load peak-to-valley fluctuation to establish the objective function; establish power battery charging and discharging constraints, power battery energy continuity constraints and power battery EQU life model constraints; construct a multi-objective optimization model for peak shaving and valley filling;

[0097] In this step, the model mainly realizes the optimization of the charging and discharging strategy of electric vehicles in the scenario of electric vehicles participating in peak shaving and valley filling. It is a multi-objective optimization. The objective function takes into account the comprehensive load of electric vehicles and battery degradation cost on the one hand, and the peak-to-valley difference and load fluctuation of peak shaving and valley filling on the other hand.

[0098] F1=C1+C bat

[0099] C1 is the comprehensive load cost, C bat The cost is the battery life loss.

[0100] Comprehensive load peak-to-valley difference:

[0101] F2=min[max(P t )-min(P t )]

[0102]

[0103] P t base is the original load, p i,t It is the charging and discharging power of the electric vehicle at each moment, and charging is positive.

[0104] Comprehensive load peak and valley fluctuations:

[0105]

[0106] By assigning weights, the multi-objective problem can be optimized into a single objective:

[0107]

[0108] The constraints of the multi-objective optimization model for peak shaving and valley filling are as follows:

[0109] (1) Considering the charging and discharging constraints of V2G power batteries

[0110] To simulate the real V2G situation, assume that there are N electric vehicles, N V2G Cars participate in the V2G protocol. For each car, it is randomly connected to and time off the grid

[0111]

[0112] P dis and P char They are the maximum discharge power (negative) and the maximum charging power respectively.

[0113] (2) Energy continuity constraints of power batteries

[0114] Constraints are set for the charging and discharging process of each electric vehicle. The relationship between the dynamic charging process of the electric vehicle and the SOC is:

[0115]

[0116]

[0117] In the formula, p i,t is the charge and discharge power in the current period, and η is the charge and discharge efficiency. Indicates the relationship between the state of charge (SOC) and the current total electrical energy of the electric vehicle.

[0118] Randomize the initial power of each EV when it is connected Expected power Set to 75% of the total power of the power battery. The sum of the initial power of each vehicle today and the total charge is not less than the expected power:

[0119]

[0120] Considering the safety and stability of electric vehicles, the available SOC of the power battery is usually smaller than the actual SOC range, limiting the SOC in each period to the available range:

[0121]

[0122] (3) Establish the power battery EQU life model constraints based on the battery EQU life model of S1

[0123] The model based on the depth of discharge must first determine the discharge depth of the battery at any time and the segment in which the machine is located. The discharge depth of the battery at time t Calculate the SOC at this moment:

[0124]

[0125] Since the parameters are piecewise linearized for the discharge depth, the following three constraints can be used to determine the segmentation of the battery energy storage discharge depth.

[0126]

[0127] In the formula, and are the upper and lower limits of the discharge depth of the dth segment respectively.

[0128] Secondly, we need to find the battery charge and discharge cycle. Since the rain flow counting method is non-convex in calculating the cycle discharge depth, it is not suitable for mixed integer linear programming. Therefore, we directly use the discharge depth at the beginning of each charge and discharge cycle as the maximum possible discharge depth of the cycle for approximate calculation. Find the moment when the energy storage charge and discharge state changes, that is, if the energy storage starts charging at time t, the 0-1 variable The value of is 1.

[0129]

[0130] is the charge and discharge state at time t, 1 is charging, 0 is discharging. Due to the handshake protocol, electric vehicles do not have the situation of neither charging nor discharging when connected to the grid. This variable is converted from p to i,t Calculated.

[0131] Determine the number of daily discharges:

[0132]

[0133] Limit the total number of equivalent cycles in a day:

[0134]

[0135] If the energy storage starts charging at time t, the equivalent full cycle number is calculated according to the discharge depth at time t-1. The left side of the inequality calculates the total equivalent cycle number in a typical day, and the right side of the inequality indicates the maximum daily equivalent cycle number required to ensure that the battery can work normally until the floating charge life is reached.

[0136] S3, solving the peak shaving and valley filling multi-objective optimization model to obtain the battery charging and discharging strategy.

[0137] By solving the above model, the charging and discharging time and corresponding power of large quantities of electric vehicles can be obtained, thereby reducing the peak load and filling the valley of local load, while minimizing the battery loss cost.

[0138] See also Figure 2 The embodiment of the present invention discloses an electric vehicle battery charging and discharging system for peak shaving and valley filling, including a battery life model building module, a multi-objective optimization model building module and a charging and discharging strategy solving module.

[0139] Among them, the battery life model construction module is used to construct the battery EQU life model and calculate the daily energy storage depreciation cost of the battery; the multi-objective optimization model construction module is used to establish the objective function based on the daily energy storage depreciation cost of the battery, taking into account the comprehensive load of electric vehicles and battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation; establish power battery charging and discharging constraints, power battery energy continuity constraints and power battery EQU life model constraints; construct a peak shaving and valley filling multi-objective optimization model; the charging and discharging strategy solving module is used to solve the peak shaving and valley filling multi-objective optimization model to obtain the battery charging and discharging strategy.

[0140] Example:

[0141] This embodiment is based on the daily load curve of a typical office building in a city, performs cluster management and control on electric vehicles connected to the office building, optimizes the charging time and power of each electric vehicle, and the basic daily load power of the office building is as follows: Figure 3 The load power shows obvious fluctuations within a day. This load curve reflects the law of power usage in office buildings and can be used as a basis for optimizing electric vehicle charging strategies.

[0142] Assume that there are 7 electric vehicles, 5 of which participate in the V2G protocol and generate bidirectional power, and the remaining 2 only accept charging regulation and do not generate reverse power. The time when all electric vehicles connect to and leave the grid is normally randomized, as shown in Figure 4.

[0143] The optimized hourly charging power of 7 electric vehicles is shown in the figure. The power of each electric vehicle is random when it is connected to the grid, and the final off-grid power is not less than the owner's expected power. The experiment is repeated four times.

[0144] Add the original load and the electric vehicle load to get the new load curve as Figure 5 shown.

[0145] By comparison, it can be seen that the charging activities of electric vehicles will increase the total load to a certain extent, but reasonable optimization can reduce the negative impact on the power grid and smooth the load curve.

[0146] The present invention compares the V2G coordination algorithm for electric vehicle charging in a single building, uses the life model based on exchange power commonly used in this field to analyze the same example, analyzes and compares the costs of the two models, and obtains the average value through multiple simulations, as shown in Table 1.

[0147] Table 1 Costs of different battery life models

[0148]

[0149] Among them, the comprehensive load cost is calculated by the maximum peak-to-valley difference of the comprehensive load and the comprehensive load variance. The comprehensive load cost of the life model based on exchange power is slightly lower, but the battery life cost is higher. Taking all factors into consideration, the life model that considers the equivalent full cycle number is more economically advantageous.

[0150] In one embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the electric vehicle battery charging and discharging method for peak shaving and valley filling.

[0151] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment of the method for charging and discharging an electric vehicle battery for peak shaving and valley filling.

[0152] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] Finally, 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for charging and discharging electric vehicle batteries for peak load shaving and valley load filling, characterized in that: The following steps are involved: Construct a battery EQU life model and calculate the daily energy storage depreciation cost of the battery; Based on the daily energy storage depreciation cost of the battery, the objective function is established by considering the comprehensive load of electric vehicles and the battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation; the power battery charging and discharging constraints, the power battery energy continuity constraints and the power battery EQU life model constraints are established; and a multi-objective optimization model for peak shaving and valley filling is constructed; The peak shaving and valley filling multi-objective optimization model is solved to obtain the battery charging and discharging strategy.

2. The method for charging and discharging electric vehicle batteries for peak load shifting and valley load filling according to claim 1, characterized in that: The steps of constructing a battery EQU life model and calculating the daily energy storage loss cost of the battery specifically include: Obtain the battery investment cost, operation and maintenance cost, and battery calendar life, and calculate the annual average cost C APV , the specific calculation formula is: In the formula, C op is the battery investment cost; C PV is the operation and maintenance cost; T calendar is the battery calendar life; r is the return on capital; Based on the average annual cost C APV Calculate the daily energy storage loss cost of the battery C bat ; Calculate the equivalent full cycle number of the battery N eq,i , the specific calculation formula is: Where, d i is the discharge depth of the i-th single charge and discharge cycle; C is the battery loss coefficient; The equivalent full cycle number of the power battery of electric vehicles is limited. The specific expression is: Where, I is the number of EV daily charging times; and It is based on the data provided by the battery manufacturer; It means ensuring that the battery can work normally until the maximum daily equivalent cycle number corresponding to the float charge life is reached.

3. The method for charging and discharging electric vehicle batteries for peak load shifting according to claim 1, characterized in that: The steps of establishing the objective function based on the daily energy storage depreciation cost of the battery, taking into account the comprehensive load of the electric vehicle and the battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation, specifically include: Considering the comprehensive load of electric vehicles and the battery degradation cost, the objective function F1 is established. The specific calculation formula is: F1=C1+C bat In the formula, C1 is the comprehensive load cost, C bat The daily energy storage cost of the battery; Considering the comprehensive load peak-to-valley difference of peak shaving and valley filling, the objective function F2 is established. The specific calculation formula is: F2=min[max(P t )-min(P t )] In the formula, is the original load, p i,t The charging and discharging power of the electric vehicle at each moment, charging is positive; Considering the peak-valley fluctuation of the comprehensive load of peak shaving and valley filling, the objective function F3 is established. The specific calculation formula is: By assigning weights to the multi-objective problem, a single-objective optimization process is performed, and the following is obtained: In the formula, is the original load; w1, w2 and w3 are weights.

4. The method for charging and discharging electric vehicle batteries for peak load shifting according to claim 1, characterized in that: The expression of the power battery charge and discharge constraint is: Where, N represents the number of electric vehicles; V2G Indicates the number of cars participating in the V2G protocol; Indicates the time when the car is connected to the power grid; Indicates the time when the car leaves the power grid; P dis is the maximum discharge power; P char is the maximum charging power.

5. The method for charging and discharging electric vehicle batteries for peak load shifting according to claim 1, characterized in that: The process of establishing the energy continuity constraint of the power battery specifically includes: Constraints are set for the charging and discharging process of each electric vehicle. The relationship between the dynamic charging process of the electric vehicle and the SOC is: In the formula, p i,t is the charge and discharge power in the current period, η is the charge and discharge efficiency; E i Indicates the current total power of the electric vehicle; SOC i Indicates the current state of charge; Randomize the initial power of each EV when it is connected Expected power Set to 75% of the total power of the power battery; the sum of the initial power of each vehicle today and the total charge is not less than the expected power. The specific expression is: Considering the safety and stability of electric vehicles, the SOC of each period is limited to the available range. The specific expression is: In the formula, Indicates the initial power of each EV when it is connected; SOC i,min Indicates the minimum charge; SOC i,max Indicates maximum power.

6. The method for charging and discharging electric vehicle batteries for peak load shifting according to claim 1, characterized in that: The process of establishing the power battery EQU life model constraint specifically includes: Determine the battery discharge depth at any time. The segment the machine is in, the battery discharge depth x at time t t DOD The specific calculation formula is as follows: x t DOD =1-x t SOC In the formula, x t DOD Indicates the discharge depth of the battery at time t; Indicates the SOC of the battery at time t; Since the parameters are piecewise linearized for the discharge depth, the constraint condition is used to obtain the segment where the battery energy storage discharge depth is located. The expression of the constraint condition is: In the formula, is the lower limit of the discharge depth of the dth segment; is the upper limit of the discharge depth of the dth segment; Find the moment when the energy storage charge and discharge state changes: In the formula, is the charge and discharge status at time t, 1 is charging and 0 is discharging; is a 0-1 variable. If the energy storage starts charging at time t, is 1; Determine the number of daily discharges: Through the battery EQU life model, the total equivalent cycle number in one day is limited, specifically: In the formula, and Based on the data provided by the battery manufacturer; t-1 is the discharge depth of the t-1th single charge and discharge cycle.

7. The method for charging and discharging electric vehicle batteries for peak load shifting and valley load filling according to claim 1, characterized in that: The step of solving the peak shaving and valley filling multi-objective optimization model to obtain the battery charging and discharging strategy specifically includes: obtaining the charging and discharging time and corresponding power of a large number of electric vehicles by solving the peak shaving and valley filling multi-objective optimization model, thereby adjusting the charging and discharging of the electric vehicle batteries.

8. An electric vehicle battery charging and discharging system for peak load shaving and valley load filling, characterized in that: include: The battery life model building module is used to build the battery EQU life model and calculate the daily energy storage depreciation cost of the battery; The multi-objective optimization model building module is used to establish the objective function based on the daily energy storage depreciation cost of the battery, taking into account the comprehensive load of electric vehicles and battery degradation cost, the comprehensive load peak-to-valley difference and the comprehensive load peak-to-valley fluctuation; establish the power battery charging and discharging constraints, the power battery energy continuity constraints and the power battery EQU life model constraints; and construct a multi-objective optimization model for peak shaving and valley filling; The charging and discharging strategy solving module is used to solve the peak shaving and valley filling multi-objective optimization model to obtain the battery charging and discharging strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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