Charging station system configuration method, device, equipment and computer readable storage medium

Abstract

The present invention discloses a charging station system configuration method, device, equipment and computer-readable storage medium, the method comprising: obtaining planning parameters of the charging station system; obtaining the expected load power and expected light intensity at each sampling moment; solving a target configuration scheme that makes the system configuration target meet the preset target requirements and satisfy the preset constraints based on the planning parameters, the expected load power and the expected light intensity, wherein the battery discharge rate and / or battery charging rate at the sampling moment are calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information. The present invention proposes a charging station system energy storage capacity configuration scheme that takes into account the dynamic loss of the energy storage battery, so that the target configuration scheme obtained by the solution is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic charging stations, and in particular to a charging station system configuration method, device, equipment and computer-readable storage medium. Background Art

[0002] With the development of new energy industry, electric vehicles, as an important part of new energy industry, have also been developed accordingly. In order to avoid the waste of social resources caused by blindly building charging stations, it is necessary to reasonably configure the capacity of charging station systems to achieve the purpose of economic investment in power stations and reasonable allocation of social resources. At present, many scholars have done a lot of research on the configuration of photovoltaic storage capacity, such as taking the electricity cost on the user side as the optimization target. However, since the life loss of the energy storage system is not included in the optimization target, the obtained configuration scheme cannot achieve the expected effect in practical application. Summary of the invention

[0003] The main purpose of the present invention is to provide a charging station system configuration method, device, equipment and computer-readable storage medium, aiming to propose a charging station system energy storage capacity configuration scheme taking into account the dynamic loss of energy storage batteries, so that the target configuration scheme obtained by solving is more accurate, and then the charging station system is configured according to the target configuration scheme, so that the system configuration target after the charging station system is actually put into use can better meet the expected requirements.

[0004] To achieve the above object, the present invention provides a method for configuring a charging station system, wherein the charging station system includes a photovoltaic power generation system and an energy storage system, and the method for configuring the charging station system includes the following steps:

[0005] Acquiring planning parameters of the charging station system;

[0006] Obtain the estimated load power and estimated light intensity at each sampling moment;

[0007] According to the planning parameters, the expected load power and the expected light intensity, solving a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints, the target configuration scheme including the target photovoltaic capacity of the photovoltaic power generation system and the target rated capacity and target rated power of the energy storage system;

[0008] Among them, the preset constraints include at least a discharge power constraint and / or a charging power constraint of the energy storage system, the discharge power constraint includes a constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes a constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or battery charging rate at the sampling moment is calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

[0009] Optionally, the planning parameters include power purchase target parameters, the system configuration targets include at least the power purchase target of the charging station system, the preset constraints also include power purchase rate constraints of the charging station system, the power purchase rate constraints are used to constrain the power purchase rate of the charging station system, and are calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the discharge power of the energy storage system and the discharge conditions at each sampling moment, and the power purchase target is calculated based on the power purchase target parameters and the power purchase rate of the charging station system.

[0010] Optionally, the step of solving a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies a preset constraint condition comprises:

[0011] Initialize a population and use the initialized population as a target population, and use each individual in the target population as a target individual, wherein an individual includes a photovoltaic capacity value of the photovoltaic power generation system, a rated capacity value and a rated power value of the energy storage system;

[0012] Solve the target charging and discharging scheme of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration scheme, wherein the target charging and discharging scheme specifies the discharge and charging conditions of the energy storage system at each sampling time;

[0013] Taking the system configuration target of the charging station system when the target individual is used as the configuration scheme and the energy storage system is charged and discharged according to the target charging and discharging scheme as the fitness value corresponding to the target individual;

[0014] Check whether the stop condition of the loop iteration is met;

[0015] If satisfied, then select the individual with the smallest fitness value from the target individuals as the target configuration scheme;

[0016] If not, individuals whose fitness values ​​meet the preset fitness conditions are selected from the target population as elite individuals, and the population composed of offspring individuals generated by crossover mutation and the elite individuals is used as the target population, and the process returns to execute the step of taking each individual in the target population as the target individual.

[0017] Optionally, the system configuration target also includes a target for unabsorbed power generation of the charging station system, the planning parameters also include target parameters for unabsorbed power generation, and the preset constraints also include a constraint on the unabsorbed power generation rate and a constraint on the charge and discharge status of the energy storage system. The constraint on the unabsorbed power generation rate is used to constrain the unabsorbed power generation rate of the charging station system, which is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the charging power of the energy storage system and the charging status at each sampling moment. The charge and discharge status constraint is used to constrain the energy storage system from being in a charging state and a discharging state at the same time. The unabsorbed power generation target is calculated based on the unabsorbed power generation target parameters and the unabsorbed power generation rate.

[0018] Optionally, the power purchase rate constraint condition is:

[0019]

[0020] The constraint condition of the power generation unabsorption rate is:

[0021]

[0022] The charging and discharging constraints are:

[0023] x t +y t ≤1

[0024] Among them, LOPE represents the electricity purchase rate, N represents the number of samples, and t l and t s represents the sampling time, P load (t) represents load power, P ESS (t) represents the charging or discharging power of the energy storage system, P RE (t) represents the power generation of the photovoltaic power station, x t is the variable representing the discharge condition, y t is a variable representing the charging status;

[0025] Optionally, the step of solving a target charging and discharging scheme of the energy storage system that makes the system configuration target of the charging station system meet preset target requirements and satisfy preset constraints when the target individual is used as a configuration scheme includes:

[0026] After converting the nonlinear charging and discharging constraints into linear constraints, a mixed integer linear programming model solving algorithm is used to solve the target charging and discharging plan of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration plan.

[0027] Optionally, the nonlinear charging and discharging condition constraint is converted into the following linear constraint:

[0028] m=x t ·P ESS (t l );

[0029] m≥x t ·P ESS (t l ) min ;

[0030] m≤x t ·P ESS (t l ) max ;

[0031] m≥P ESS (t l )-(1-x t )·P ESS (t l ) max ;

[0032] m≤P ESS (t l )-(1-x t )·P ESS (t l ) min ;

[0033] n=y t ·P RE (t s );

[0034] n≥y t ·P RE (t s ) min ;

[0035] n≤y t ·P RE (t s ) max ;

[0036] n≥P RE (t s )-(1-y t )·PRE (t s ) max ;

[0037] n≤P RE (t s )-(1-y t )·P RE (t s ) min .

[0038] Optionally, the system configuration objectives also include construction and maintenance objectives of the energy storage system, the planning parameters also include the planned service life of the charging station system and the number of replacements of the energy storage system, the purchase coefficient of the energy storage capacity, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter and the maintenance coefficient per unit power, the construction and maintenance objectives include capacity maintenance objectives and power maintenance objectives, the capacity maintenance objective is calculated based on the number of replacements, the purchase coefficient of the energy storage capacity and the rated capacity of the energy storage system, and the power maintenance objective is calculated based on the number of replacements, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter, the maintenance coefficient per unit power, the planned service life and the rated power of the energy storage system.

[0039] Optionally, the charging station system configuration method further includes:

[0040] The capacity attenuation coefficient of the battery of the energy storage system at each stage is obtained as the battery capacity attenuation characteristic information, so that when calculating the actual capacity at the sampling moment, the target capacity attenuation coefficient corresponding to the stage of the battery at the current sampling moment is determined, and the capacity attenuation degree of the battery at the current sampling moment is calculated according to the target capacity attenuation coefficient, the current life loss degree of the battery, and the capacity attenuation degree of the battery at the previous sampling moment, and the actual capacity of the battery at the current sampling moment is calculated according to the capacity attenuation degree of the battery at the current sampling moment and the rated capacity of the energy storage system.

[0041] Optionally, when calculating the actual capacity at the sampling moment, when the stage of the battery at the current sampling moment is different from the stage of the battery at the previous sampling moment, an initial average capacity attenuation degree corresponding to the stage of the battery at the current sampling moment is obtained as the capacity attenuation degree of the battery at the previous sampling moment, so as to calculate the actual capacity at the current sampling moment;

[0042] When the battery is a new battery at the current sampling moment, the preset new battery capacity attenuation degree is used as the capacity attenuation degree of the battery at the previous sampling moment to calculate the actual capacity at the current sampling moment.

[0043] Optionally, when calculating the actual capacity at the sampling moment, when the calculated capacity attenuation degree at the current sampling moment is less than a preset threshold, it is determined that the battery is replaced with a new battery at the next sampling moment.

[0044] To achieve the above object, the present invention further provides a charging station system configuration device, wherein the charging station system includes a photovoltaic power generation system and an energy storage system, and the charging station system configuration device includes:

[0045] A first acquisition module, used to acquire planning parameters of the charging station system;

[0046] The second acquisition module is used to obtain the estimated load power and the estimated light intensity at each sampling moment;

[0047] a solution module, configured to solve a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints according to the planning parameters, the expected load power and the expected light intensity, wherein the target configuration scheme includes a target photovoltaic capacity of the photovoltaic power generation system and a target rated capacity and a target rated power of the energy storage system;

[0048] Among them, the preset constraints include at least a discharge power constraint and / or a charging power constraint of the energy storage system, the discharge power constraint includes a constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes a constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or battery charging rate at the sampling moment is calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

[0049] To achieve the above-mentioned purpose, the present invention also provides a charging station system configuration device, which includes: a memory, a processor, and a charging station system configuration program stored in the memory and executable on the processor, and when the charging station system configuration program is executed by the processor, the steps of the charging station system configuration method as described above are implemented.

[0050] In addition, to achieve the above objectives, the present invention also proposes a computer-readable storage medium, on which a charging station system configuration program is stored, and when the charging station system configuration program is executed by a processor, the steps of the charging station system configuration method described above are implemented.

[0051] In the present invention, by obtaining the planning parameters of the charging station system, wherein the planning parameters include the target parameters for purchasing electricity; obtaining the expected load power and the expected light intensity at each sampling moment; solving the target configuration scheme of the charging station system so that the system configuration target of the charging station system meets the preset constraints according to the planning parameters, the expected load power and the expected light intensity, the target configuration scheme includes the target photovoltaic capacity of the photovoltaic power generation system and the target rated capacity and target rated power of the energy storage system; wherein the preset constraints include at least the discharge power constraint and / or the charging power constraint of the energy storage system, the discharge power constraint includes the constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes the constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or the battery charging rate at the sampling moment are calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

[0052] Compared to the charging station configuration method that does not consider the life attenuation of the energy storage system, the present invention takes into account the life attenuation of the energy storage system by performing calculations based on the actual capacity of the energy storage system in the calculation of the battery discharge rate and / or the battery charging rate, and calculates the actual capacity based on the battery attenuation characteristic information of the energy storage system, so that the target configuration scheme obtained by solving is more accurate, and then the charging station system is configured according to the target configuration scheme, and the system configuration target after the charging station system is actually put into use can better meet the expected requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of the structure of the hardware operating environment involved in the embodiment of the present invention;

[0054] Figure 2 It is a flowchart of a first embodiment of a method for configuring a charging station system according to the present invention;

[0055] Figure 3 It is a functional module diagram of a preferred embodiment of the charging station system configuration device of the present invention.

[0056] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0058] like Figure 1 As shown, Figure 1It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0059] It should be noted that the charging station system configuration device in the embodiment of the present invention may be a smart phone, a personal computer, a server, etc., and is not specifically limited here. The charging station system includes a photovoltaic power generation system and an energy storage system.

[0060] like Figure 1 As shown, the charging station system configuration device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0061] Those skilled in the art will understand that Figure 1 The device structure shown in the figure does not constitute a limitation on the configuration device of the charging station system, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0062] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a charging station system configuration program. The operating system is a program that manages and controls the hardware and software resources of the device, and supports the operation of the charging station system configuration program and other software or programs. Figure 1 In the device shown, the user interface 1003 is mainly used for data communication with the client; the network interface 1004 is mainly used for establishing a communication connection with the server; and the processor 1001 can be used to call the charging station system configuration program stored in the memory 1005 and perform the following operations:

[0063] Acquiring planning parameters of the charging station system;

[0064] Obtain the estimated load power and estimated light intensity at each sampling moment;

[0065] According to the planning parameters, the expected load power and the expected light intensity, solving a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints, the target configuration scheme including the target photovoltaic capacity of the photovoltaic power generation system and the target rated capacity and target rated power of the energy storage system;

[0066] Among them, the preset constraints include at least a discharge power constraint and / or a charging power constraint of the energy storage system, the discharge power constraint includes a constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes a constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or battery charging rate at the sampling moment is calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

[0067] Furthermore, the planning parameters include power purchase target parameters, the system configuration target includes at least the power purchase target of the charging station system, the preset constraints also include a power purchase rate constraint of the charging station system, the power purchase rate constraint is used to constrain the power purchase rate of the charging station system, and is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the discharge power of the energy storage system and the discharge conditions at each sampling time, and the power purchase target is calculated based on the power purchase target parameters and the power purchase rate of the charging station system.

[0068] Furthermore, the operation of solving the target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies the preset constraint condition includes:

[0069] Initialize a population and use the initialized population as a target population, and use each individual in the target population as a target individual, wherein an individual includes a photovoltaic capacity value of the photovoltaic power generation system, a rated capacity value and a rated power value of the energy storage system;

[0070] Solve the target charging and discharging scheme of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration scheme, wherein the target charging and discharging scheme specifies the discharge and charging conditions of the energy storage system at each sampling time;

[0071] Taking the system configuration target of the charging station system when the target individual is used as the configuration scheme and the energy storage system is charged and discharged according to the target charging and discharging scheme as the fitness value corresponding to the target individual;

[0072] Check whether the stop condition of the loop iteration is met;

[0073] If satisfied, then select the individual with the smallest fitness value from the target individuals as the target configuration scheme;

[0074] If not, individuals whose fitness values ​​meet the preset fitness conditions are selected from the target population as elite individuals, and the population composed of offspring individuals generated by crossover mutation and the elite individuals is used as the target population, and the operation of using each individual in the target population as the target individual is returned to be executed.

[0075] Furthermore, the system configuration target also includes an unabsorbed power generation target of the charging station system, the planning parameters also include unabsorbed power generation target parameters, the preset constraints also include unabsorbed power generation rate constraints and charging and discharging condition constraints of the energy storage system, the unabsorbed power generation rate constraints are used to constrain the unabsorbed power generation rate of the charging station system, which is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the charging power of the energy storage system and the charging condition at each sampling time, the charging and discharging condition constraints are used to constrain the energy storage system from being in the charging state and the discharging state at the same time, and the unabsorbed power generation target is calculated based on the unabsorbed power generation target parameters and the unabsorbed power generation rate.

[0076] Furthermore, the power purchase rate constraint condition is:

[0077]

[0078] The constraint condition of the power generation unabsorption rate is:

[0079]

[0080] The charging and discharging constraints are:

[0081] x t +y t ≤1

[0082] Among them, LOPE represents the electricity purchase rate, N represents the number of samples, and t l and t s represents the sampling time, P load (t) represents load power, P ESS (t) represents the charging or discharging power of the energy storage system, P RE (t) represents the power generation of the photovoltaic power station, x t is the variable representing the discharge condition, y t is a variable representing the charging status;

[0083] Further, the operation of solving the target charging and discharging scheme of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraint conditions when the target individual is used as the configuration scheme includes:

[0084] After converting the nonlinear charging and discharging constraints into linear constraints, a mixed integer linear programming model solving algorithm is used to solve the target charging and discharging plan of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration plan.

[0085] Furthermore, the nonlinear charging and discharging condition constraint is converted into the following linear constraint:

[0086] m=x t ·P ESS (t l );

[0087] m≥x t ·P ESS (t l ) min ;

[0088] m≤x t ·P ESS (t l ) max ;

[0089] m≥P ESS (t l )-(1-x t )·P ESS (t l ) max ;

[0090] m≤P ESS (t l )-(1-x t )·P ESS (t l ) min ;

[0091] n=y t ·P RE (t s );

[0092] n≥y t ·P RE (t s ) min ;

[0093] n≤y t ·PRE (t s ) max ;

[0094] n≥P RE (t s )-(1-y t )·P RE (t s ) max ;

[0095] n≤P RE (t s )-(1-y t )·P RE (t s ) min .

[0096] Furthermore, the system configuration objectives also include construction and maintenance objectives of the energy storage system, the planning parameters also include the planned service life of the charging station system and the number of replacements of the energy storage system, the purchase coefficient of the energy storage capacity, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter and the maintenance coefficient per unit power, the construction and maintenance objectives include capacity maintenance objectives and power maintenance objectives, the capacity maintenance objective is calculated based on the number of replacements, the purchase coefficient of the energy storage capacity and the rated capacity of the energy storage system, and the power maintenance objective is calculated based on the number of replacements, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter, the maintenance coefficient per unit power, the planned service life and the rated power of the energy storage system.

[0097] Furthermore, the processor 1001 may also be used to call the charging station system configuration program stored in the memory 1005 to perform the following operations:

[0098] The capacity attenuation coefficient of the battery of the energy storage system at each stage is obtained as the battery capacity attenuation characteristic information, so that when calculating the actual capacity at the sampling moment, the target capacity attenuation coefficient corresponding to the stage of the battery at the current sampling moment is determined, and the capacity attenuation degree of the battery at the current sampling moment is calculated according to the target capacity attenuation coefficient, the current life loss degree of the battery, and the capacity attenuation degree of the battery at the previous sampling moment, and the actual capacity of the battery at the current sampling moment is calculated according to the capacity attenuation degree of the battery at the current sampling moment and the rated capacity of the energy storage system.

[0099] Further, when calculating the actual capacity at the sampling moment, when the stage of the battery at the current sampling moment is different from the stage of the battery at the previous sampling moment, the initial average capacity attenuation degree corresponding to the stage of the battery at the current sampling moment is obtained as the capacity attenuation degree of the battery at the previous sampling moment, so as to calculate the actual capacity at the current sampling moment;

[0100] When the battery is a new battery at the current sampling moment, the preset new battery capacity attenuation degree is used as the capacity attenuation degree of the battery at the previous sampling moment to calculate the actual capacity at the current sampling moment.

[0101] Further, when calculating the actual capacity at the sampling moment, when the calculated capacity attenuation degree at the current sampling moment is less than a preset threshold, it is determined that the battery is replaced with a new battery at the next sampling moment.

[0102] Based on the above structure, various embodiments of a charging station system configuration method are proposed.

[0103] Reference Figure 2 , Figure 2 FIG. 1 is a flow chart of a first embodiment of a method for configuring a charging station system according to the present invention.

[0104] The embodiment of the present invention provides an embodiment of a charging station system configuration method. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than here. In this embodiment, the execution subject of the charging station system configuration method may be a personal computer, a smart phone or other device, which is not limited in this embodiment. For the sake of ease of description, the following description of each embodiment is omitted. In this embodiment, the charging station system configuration method includes:

[0105] Step S10, obtaining planning parameters of the charging station system;

[0106] In this embodiment, the charging station system includes a photovoltaic power generation system and an energy storage system. The photovoltaic power generation system is used to generate electricity through photovoltaic modules for charging loads (such as electric vehicles). Excess electric energy can be stored by the energy storage battery of the energy storage system. When the photovoltaic power generation system generates insufficient electricity, the energy storage system can be discharged to charge the load. Configuring the charging station system means determining the photovoltaic capacity of the photovoltaic power generation system in the charging station system, the rated capacity and rated power of the energy storage system. The goal of the configuration is to determine a target configuration scheme so that when the charging station system is constructed according to the photovoltaic capacity (hereinafter referred to as the target photovoltaic capacity), rated capacity (hereinafter referred to as the target rated capacity) and rated power (hereinafter referred to as the target rated power) in the target configuration scheme, the system configuration target of the charging station system can meet certain target requirements. The target requirement can be set specifically as needed, for example, it can be set to a system configuration target that is lower than a threshold, or it can be set to be the lowest of all optional configuration schemes, which is not limited in this embodiment.

[0107] When configuring the charging station system, the planning parameters of the charging station system can be obtained first. The planning parameters can be uploaded after being set by the technical staff as needed, and the method of obtaining the planning parameters is to obtain the uploaded data. The planning parameters may include parameters related to the selected configuration scheme but fixed or manually set during the configuration of the charging station system, including which parameters are related to the calculation method of the selected system configuration target. In one embodiment, the system configuration target may include at least a power purchase target, then the planning parameters may include a power purchase target parameter, which is used to calculate the power purchase target in combination with the power purchase rate. Power purchase means that when the power generated by the photovoltaic power generation system of the charging station system cannot meet the load, it is necessary to purchase power from the power grid. The power purchase target may be a target calculated based on the power purchase rate. By optimizing or limiting the power purchase target, the effect of limiting the power purchase rate can be achieved. It should be noted that the calculation method of the power purchase target parameter and the power purchase rate in combination with the power purchase target can also be selected as needed, and is not limited in this embodiment. In this embodiment, the system configuration target may only include the power purchase target, or may also include other types of targets, such as the construction and maintenance targets of the energy storage system, which are not limited in this embodiment.

[0108] Step S20, obtaining the estimated load power and the estimated light intensity at each sampling moment;

[0109] Sampling time refers to sampling of the charging station system within the expected service life, so as to calculate the system configuration target of the charging station system by estimating the working state of the charging station system at each sampling time. To estimate the working state of the charging station system at each sampling time, it is necessary to use the load power and light intensity at each sampling time. Since the charging station system has not yet been put into use, the load power and light intensity at each sampling time are expected data, so they are referred to as expected load power and expected light intensity below. In this embodiment, there is no restriction on the method of obtaining the expected load power and expected light intensity at each sampling time. For example, in one embodiment, the expected load power and expected light intensity at each sampling time can be directly uploaded by a technician. For example, in another embodiment, multiple typical day scenes can be generated for each year within the expected service life of the charging station system, for example, 4 typical day scenes representing each quarter of the charging station system in that year are generated; in each typical day scene, it is divided into multiple planning time periods (each planning time period corresponds to a sampling moment), assuming that the load forecast data and environmental parameters such as light remain unchanged in each time period, for example, it is divided into 96 time periods, and each time period is separated by a quarter of an hour; wherein the expected load power at each sampling moment can be obtained according to the charging station load forecasting technology, and the expected light intensity at each sampling moment can be obtained according to the historical light data statistics.

[0110] Step S30, solving a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints, the target configuration scheme including a target photovoltaic capacity of the photovoltaic power generation system and a target rated capacity and a target rated power of the energy storage system;

[0111] After obtaining the planning parameters, the expected load power at each sampling time, and the expected light intensity, a configuration scheme of the charging station system can be solved so that the system configuration target of the charging station system meets the preset constraints. This configuration scheme is the target configuration scheme. That is, it is necessary to solve a target configuration scheme so that the system configuration target of the charging station system meets the preset constraints. Furthermore, in one embodiment, a target requirement can be set according to the actual situation, and the target configuration scheme obtained by solving the target requirement also needs to meet the target requirement.

[0112] The preset constraints include at least the discharge power constraint and / or the charging power constraint of the energy storage system. That is, in a specific implementation, the preset constraints may include only the discharge power constraint of the energy storage system, or may include some other constraints, which may be set as needed and are not limited in this embodiment.

[0113] The discharge power constraint condition includes a constraint condition consisting of the rated power of the energy storage system and the battery discharge rate. In a specific implementation, the discharge power constraint condition may be that the discharge power of the energy storage system is the smaller of the rated power of the energy storage system and the battery discharge rate, that is, the battery discharge rate of the energy storage system cannot be greater than the rated power (the discharge power of the energy storage converter). The actual capacity of the energy storage system is required to calculate the battery discharge rate at the sampling time. Since the energy storage battery has a capacity decay phenomenon, the actual capacity of the energy storage system is dynamically changing. When the capacity decay of the energy storage battery of the energy storage system is not considered, the rated capacity of the energy storage system is directly used to participate in the calculation, which will lead to inaccurate calculation of the battery discharge rate at the sampling time, and then the target configuration scheme obtained by the solution is not consistent with the expectation. The battery discharge rate at the sampling time is calculated based on the actual capacity of the energy storage system at the sampling time, and the specific calculation method is not limited in this embodiment. The actual capacity at the sampling time is calculated based on the rated capacity of the energy storage system and the battery capacity decay characteristic information, and the specific calculation method is not limited in this embodiment. Among them, the battery capacity decay characteristic information can be obtained in advance, for example, uploaded by a technician.

[0114] The charging power constraint condition includes a constraint condition consisting of the rated power of the energy storage system and the battery charging rate. The battery charging rate at the sampling time is calculated based on the actual capacity of the energy storage system at the sampling time. In a specific implementation, the charging power constraint condition may be that the charging power of the energy storage system is the smaller of the rated power of the energy storage system and the battery charging rate, that is, the battery charging rate of the energy storage system cannot be greater than the rated power (charging power of the energy storage converter). The actual capacity of the energy storage system is required to calculate the battery charging rate at the sampling time. Since the energy storage battery has a capacity decay phenomenon, the actual capacity of the energy storage system changes dynamically. When the capacity decay of the energy storage battery of the energy storage system is not considered, the rated capacity of the energy storage system is directly used to participate in the calculation, which will lead to inaccurate calculation of the battery charging rate at the sampling time, and then the target configuration scheme obtained by the solution is not consistent with the expectation. In this embodiment, the battery charging rate at the sampling time is calculated based on the actual capacity of the energy storage system at the sampling time, and the specific calculation method is not limited in this embodiment.

[0115] Furthermore, in one embodiment, the preset constraint condition also includes a power purchase rate constraint condition for the charging station system. The power purchase rate constraint condition is used to constrain the power purchase rate of the charging station system, which is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the discharge power of the energy storage system, and the discharge conditions at each sampling time. The specific calculation method is not limited in this embodiment. Among them, the discharge condition characterizes whether the energy storage system is discharged. When discharging, the energy storage system is in a discharging state, and when not discharging, the energy storage system is in a non-discharging state. The discharge conditions of the energy storage system at each sampling time can be uploaded by the technicians after configuration based on experience, or can be used as one of the configuration schemes.

[0116] In this embodiment, there is no limitation on the method for solving the target configuration solution, and a commonly used method for solving planning problems can be used.

[0117] After the target configuration scheme is solved, the target configuration scheme can be output so that the technicians can configure the charging station system according to the target configuration scheme, so that the system configuration target can meet the expected requirements after the charging station system is actually put into use. Compared with the charging station configuration method that does not consider the life decay of the energy storage system, this embodiment calculates based on the actual capacity of the energy storage system in the calculation of the battery discharge rate and / or the battery charging rate, and calculates the actual capacity based on the battery decay characteristic information of the energy storage system, taking into account the life decay of the energy storage system, so that the target configuration scheme obtained is more accurate, and thus the configuration of the charging station system according to the target configuration scheme, the system configuration target after the charging station system is actually put into use can be more in line with the expected requirements.

[0118] Furthermore, based on the above first embodiment, a second embodiment of the charging station system configuration method of the present invention is proposed. In this embodiment, the charging and discharging scheme of the energy storage system can also be used as a configuration object. In this regard, in this embodiment, a double-layer optimization solution can be used to solve the target configuration scheme. The photovoltaic capacity of the photovoltaic power generation system, the rated capacity and rated power of the energy storage system can be used as outer variables, and the discharge and charging conditions of the energy storage system at each sampling time can be used as inner variables.

[0119] The outer layer is solved by a global optimization genetic algorithm (GA), and the inner layer is a mixed integer linear model that can be solved using mature commercial solving software.

[0120] Further, in one embodiment, step S30 includes:

[0121] Step S301, initializing a population and taking the initialized population as a target population, taking each individual in the target population as a target individual, wherein one individual includes a photovoltaic capacity value of the photovoltaic power generation system, a rated capacity value and a rated power value of the energy storage system;

[0122] The population can be initialized first, and the initialized population is used as the target population. A population includes multiple individuals, and each individual includes a photovoltaic capacity value of the photovoltaic power generation system, a rated capacity value of the energy storage system, and a rated power value. The method of initializing the population can be randomly selected or selected based on experience, which is not limited in this embodiment. Each individual in the target population is called a target individual for distinction.

[0123] Step S302, solving a target charging and discharging scheme of the energy storage system that makes the system configuration target of the charging station system meet the preset target requirements and satisfy the preset constraint conditions when the target individual is used as the configuration scheme, wherein the target charging and discharging scheme specifies the discharge and charging conditions of the energy storage system at each sampling time;

[0124] For each target individual, the inner layer solves the charging and discharging scheme of the energy storage system (hereinafter referred to as the target charging and discharging scheme) that makes the system configuration target of the charging station system meet the preset target requirements and satisfy the preset constraints when the target individual is used as the configuration scheme of the charging station system. That is, when the target individual is used as the configuration scheme of the charging station system, it is necessary to solve a target charging and discharging scheme so that the system configuration target of the charging station system meets the preset target requirements, and the target charging and discharging scheme satisfies the preset constraints. The inner layer solution is not limited in this embodiment, for example, the YALMIP tool can be used for solution.

[0125] Step S303, taking the system configuration target of the charging station system when the target individual is used as a configuration scheme and the energy storage system is charged and discharged according to the target charging and discharging scheme as the fitness value corresponding to the target individual;

[0126] After solving the target charging and discharging plan corresponding to the target individual, the configuration plan of the charging station system according to the target individual and the system configuration target of the charging station system when the energy storage system is charged and discharged according to the target charging and discharging plan can be used as the fitness value corresponding to the target individual.

[0127] Step S304, detecting whether the stop condition of the loop iteration is met;

[0128] After obtaining the fitness value corresponding to each target individual in the target population, it is possible to detect whether the stop condition of the loop iteration is met. The stop condition can be set as needed, such as when the number of iterations reaches a preset number, or when the duration of the loop iteration reaches a preset duration, or when the target individual of the current iteration is the same as the target individual of the previous iteration and does not change, etc.

[0129] Step S305: if the conditions are met, then select the individual with the smallest fitness value from the target individuals as the target configuration scheme;

[0130] Step S306: If not satisfied, individuals whose fitness values ​​meet the preset fitness conditions are selected from the target population as elite individuals, and the population composed of the offspring individuals generated by crossover mutation and the elite individuals is used as the target population, and the process returns to execute the step of using each individual in the target population as a target individual in step S301.

[0131] If it is detected that the stopping condition of the loop iteration is met, the individual with the smallest fitness value can be selected from the target individuals as the target configuration scheme.

[0132] If it is detected that the stopping condition of the loop iteration is not met, individuals whose fitness values ​​meet the preset fitness conditions can be selected from the target population as elite individuals, and offspring individuals can be generated through the crossover mutation method in the genetic algorithm. The population composed of the offspring individuals and the elite individuals is used as the new target population, and then the process returns to execute the steps of taking each individual in the target population as the target individual and solving the target charge and discharge plan for the target individual. The loop is iterated until the stopping condition is met, and the target configuration plan and the target charge and discharge plan can be obtained.

[0133] Furthermore, in one embodiment, the system configuration target also includes a target for unabsorbed power generation of the charging station system. The target for unabsorbed power generation can be a target calculated based on the target parameters for unabsorbed power generation and the rate of unabsorbed power generation. By optimizing or in other words, the target for unabsorbed power generation, the effect of the rate of unabsorbed power generation can be achieved. The specific calculation method is not limited in this embodiment. Unabsorbed power generation refers to the fact that there is surplus electricity generated by the photovoltaic power generation system after supplying power to the load and outputting it to the energy storage system for storage. The planning parameters also include target parameters for unabsorbed power generation. The preset constraints also include constraints on the rate of unabsorbed power generation and constraints on the charging and discharging conditions of the energy storage system. The constraint on the rate of unabsorbed power generation is used to constrain the rate of unabsorbed power generation of the charging station system, which is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the charging power of the energy storage system, and the charging conditions at each sampling time. The specific calculation method is not limited in this embodiment. Among them, the charging status indicates whether the energy storage system is charged or not. When charging, the energy storage system is in a charging state, and when not charging, the energy storage system is in an uncharged state. The charging status of the energy storage system at each sampling time can be uploaded by the technicians based on experience configuration, or can be used as an item in the configuration plan. The charging and discharging status constraint is used to constrain the energy storage system from being in a charging state and a discharging state at the same time.

[0134] In this embodiment, by considering the target of unconsumed power generation in the system configuration target, the target configuration scheme obtained by solving can make the power purchase target of the charging station system meet expectations while the target of unconsumed power generation can also meet expectations.

[0135] Furthermore, in one embodiment, the discharge power constraint condition may be:

[0136]

[0137] P d，max is the discharge power at sampling time t, P max，d is the rated power of the energy storage system, E c is the actual capacity at sampling time t-1, SOC min is the minimum state of charge of the battery, δ is the self-discharge rate of the battery, SOC(t-1) is the state of charge of the battery at sampling time t-1, △t is the interval between two sampling times, η d Indicates the discharge efficiency of the battery.

[0138] Furthermore, in one embodiment, the charging power constraint condition may be:

[0139]

[0140] Among them, P c，max is the charging power at sampling time t, Pmax，c is the rated power of the energy storage system, E c is the actual capacity at sampling time t-1, SOC max is the maximum state of charge of the battery, δ is the self-discharge rate of the battery, SOC(t-1) is the state of charge of the battery at sampling time t-1, △t is the interval between two sampling times, η c Indicates the charging efficiency of the battery.

[0141] Furthermore, in one embodiment, the power purchase rate constraint condition is:

[0142]

[0143] The constraint condition of the power generation unabsorption rate is:

[0144]

[0145] The charging and discharging constraints are:

[0146] x t +y t ≤1

[0147] Among them, LOPE represents the electricity purchase rate, N represents the number of samples, and t l and t s represents the sampling time, P load (t) represents load power, P ESS (t) represents the charging or discharging power of the energy storage system, P RE (t) represents the power generation of the photovoltaic power station, x t is the variable representing the discharge condition, y t is a variable representing the charging status. t and t The value is 1 or 0. t When the value is 1, it means that the energy storage battery is in a discharging state, and when the value is 0, it means that the energy storage battery is not in a discharging state. t When the value is 1, it indicates that the energy storage battery is in a charging state, and when the value is 0, it indicates that the energy storage battery is in an uncharged state.

[0148] Furthermore, in one embodiment, the preset constraint condition may also include a total power balance formula of the charging station system: P ESS (t)+P RE (t) <P Load (t)

[0149] Further, in one embodiment, step S302 includes:

[0150] Step S3021, after converting the nonlinear charging and discharging constraints into linear constraints, a mixed integer linear programming model solving algorithm is used to solve the target charging and discharging scheme of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration scheme.

[0151] Due to x t and t The value is 0 or 1, which is a nonlinear variable. The charge and discharge condition constraint is a nonlinear constraint. Therefore, in this embodiment, the nonlinear charge and discharge condition constraint can be converted into a linear constraint first, and then the solution algorithm of the mixed integer linear programming model is used to solve it to improve the solution rate. The method for converting the nonlinear charge and discharge condition constraint into a linear constraint can be selected as needed and is not limited in this embodiment.

[0152] Furthermore, in one embodiment, the nonlinear charge and discharge condition constraint is converted into the following linear constraint:

[0153] m=x t ·P ESS (t l );

[0154] m≥x t ·P ESS (t l ) min ;

[0155] m≤x t ·P ESS (t l ) max ;

[0156] m≥P ESS (t l )-(1-x t )·P ESS (t l ) max ;

[0157] m≤P ESS (t l )-(1-x t )·P ESS (t l ) min ;

[0158] n=y t ·P RE (t s );

[0159] n≥yt ·P RE (t s ) min ;

[0160] n≤y t ·P RE (t s ) max ;

[0161] n≥P RE (t s )-(1-y t )·P RE (t s ) max ;

[0162] n≤P RE (t s )-(1-y t )·P RE (t s ) min .

[0163] Furthermore, based on the above-mentioned first and / or second embodiments, a third embodiment of the method for configuring a charging station system of the present invention is proposed. In this embodiment, the system configuration target may also include a construction and maintenance target for the energy storage system. By optimizing or limiting the power purchase target, the effect of limiting the construction and maintenance efforts required for the energy storage system can be achieved. By considering the construction and maintenance targets of the energy storage system in the system configuration target, the target configuration scheme obtained by solving can make the power purchase target of the charging station system meet expectations while the construction and maintenance targets of the energy storage system can also meet expectations.

[0164] Planning parameters may also include the planned service life of the charging station system and the number of replacements of the energy storage system, the purchase factor of the energy storage capacity, the purchase factor per unit power, the unit factor of the energy storage converter and the maintenance factor per unit power.

[0165] The construction maintenance target includes a capacity maintenance target and a power maintenance target. The capacity maintenance target is calculated based on the number of replacements, the purchase coefficient of the energy storage capacity, and the rated capacity of the energy storage system. The specific calculation method is not limited in this embodiment. The power maintenance target is calculated based on the number of replacements, the purchase coefficient per unit power, the unit coefficient of the energy storage converter, the maintenance coefficient per unit power, the planned service life, and the rated power of the energy storage system. The specific calculation method is not limited in this embodiment.

[0166] For example, in one embodiment, the construction and maintenance target of the energy storage system is expressed as f(E ESS ,P ESS ), the calculation method of construction maintenance target can be as follows:

[0167] f(E ESS ,P ESS )=(1+N REP )K e E ESS +(K p +K af +K om L PROJ +N REP K p ) ESS

[0168] Among them, E ESS Represents the rated capacity of the energy storage system, P ESS Indicates the rated power of the energy storage system, N REP Indicates the number of times the energy storage system is replaced, K e Represents the purchase factor of energy storage capacity, K p Indicates the purchase factor of the energy storage system per unit power, K af Represents the unit coefficient of the energy storage converter, K om Indicates the maintenance factor per unit power of the energy storage system, L PROJ Indicates the planned service life of the charging station system.

[0169] Furthermore, in one embodiment, the system configuration target may include a construction and maintenance target, a power purchase target, and a power generation and non-consumption target of the energy storage system. Furthermore, the calculation method of the system configuration target may be as follows:

[0170] f FIT1 =f(E ESS ,P ESS )+α·ABS(LOEP-LOEP max )+β·ABS(SOEP-SOEP max )

[0171] Among them, LOEP represents the electricity purchase rate, LOEP max represents the maximum power purchase rate, and α and LOEP max as the target coefficient for electricity purchase; SOEP represents the rate of unconsumed power generation, SOEP max represents the maximum unconsumed power generation rate, and β and SOEP max As the target coefficient for unconsumed power generation.

[0172] Further, based on the above-mentioned first, second and / or third embodiments, a fourth embodiment of the charging station system configuration method of the present invention is proposed. In this embodiment, the charging station system configuration method further includes:

[0173] Step S40, obtaining the capacity attenuation coefficient of the battery of the energy storage system at each stage as the battery capacity attenuation characteristic information, so as to be used for calculating the actual capacity at the sampling moment, by determining the target capacity attenuation coefficient corresponding to the stage of the battery at the current sampling moment, and calculating the capacity attenuation degree of the battery at the current sampling moment according to the target capacity attenuation coefficient, the current life loss degree of the battery and the capacity attenuation degree of the battery at the previous sampling moment, and calculating the actual capacity of the battery at the current sampling moment according to the capacity attenuation degree of the battery at the current sampling moment and the rated capacity of the energy storage system.

[0174] In this embodiment, before the actual capacity at the sampling moment needs to be calculated, the capacity attenuation coefficient of the battery of the energy storage system at each stage can be obtained as the battery capacity attenuation characteristic information. Among them, the capacity attenuation coefficient is the slope of the relationship curve between the capacity attenuation degree of the battery and the life loss degree of the battery. In this embodiment, the life of the battery is divided into multiple stages, and the capacity attenuation coefficient of each stage is considered to be the same. In a specific implementation, the capacity attenuation coefficient of the battery at each stage can be uploaded by a technician. When calculating the actual capacity at the sampling moment, the capacity attenuation coefficient corresponding to the multiple stages of the battery at the current sampling moment (hereinafter referred to as the target capacity attenuation coefficient) can be determined. The capacity attenuation degree of the battery at the current sampling moment is calculated based on the target capacity attenuation coefficient, the current life loss degree of the battery, and the capacity attenuation degree of the battery at the previous sampling moment. The actual capacity of the battery at the current sampling moment is calculated based on the capacity attenuation degree of the battery at the current sampling moment and the rated capacity of the energy storage system. The specific calculation method is not limited in this embodiment. For example, in one implementation, the capacity attenuation degree S at the sampling moment t OH (t) is calculated as:

[0175]

[0176] Among them, j represents the stage, represents the capacity attenuation coefficient of the jth stage.

[0177] The actual capacity E at sampling time t c =S OH (t)*E ESS .

[0178] Further, in one embodiment, when calculating the actual capacity at the sampling moment, when the stage of the battery at the current sampling moment is different from the stage of the battery at the previous sampling moment, the initial average capacity decay degree corresponding to the stage of the battery at the current sampling moment is obtained as the capacity decay degree of the battery at the previous sampling moment, so as to calculate the actual capacity at the current sampling moment. It should be noted that the capacity decay degree of the battery at the beginning of each stage can be preset, that is, each stage can correspond to an initial average capacity decay degree. When the stage of the battery at the current sampling moment is different from the stage of the battery at the previous sampling moment, the initial average capacity decay degree corresponding to the stage of the battery at the current sampling moment can be obtained as the capacity decay degree of the battery at the previous sampling moment, which is used to calculate the actual capacity at the current sampling moment, that is, the capacity decay degree of the battery at the current sampling moment can be calculated based on the target capacity decay coefficient, the current life loss degree of the battery and the initial average capacity decay degree of the stage of the battery at the current sampling moment. The initial average capacity decay degree of each stage may be uploaded by a technician, or may be calculated based on a relationship curve between the capacity decay degree of the battery uploaded by the technician and the life loss degree of the battery. For example, in one embodiment, the capacity decay coefficient and the initial average capacity decay degree of each stage may be calculated as follows:

[0179]

[0180]

[0181] Furthermore, in one embodiment, when the battery is a new battery at the current sampling moment, the preset new battery capacity attenuation degree is used as the capacity attenuation degree of the battery at the previous sampling moment to calculate the actual capacity at the current sampling moment. Since during the use of the energy storage system, as the capacity of the battery decays, the battery will be replaced when it reaches a certain degree, so at a certain sampling moment, the battery may be replaced with a new battery. When the battery is a new battery at the current sampling moment, the preset new battery capacity attenuation degree (generally set to 1) is used as the capacity attenuation degree of the battery at the previous sampling moment to calculate the actual capacity at the current sampling moment, that is, the capacity attenuation degree of the battery at the current sampling moment can be calculated based on the target capacity attenuation coefficient, the current life loss degree of the battery, and the preset new battery capacity attenuation degree.

[0182] Furthermore, in one embodiment, when calculating the actual capacity at the sampling moment, when the calculated capacity decay degree at the current sampling moment is less than a preset threshold, it is determined that the battery will be replaced with a new battery at the next sampling moment. The condition for determining battery replacement may be that the capacity decay degree of the battery is less than a preset threshold. Then, when the capacity decay degree at the current sampling moment is less than the preset threshold, it can be determined that the battery will be replaced with a new battery at the next sampling moment. Then, at the next sampling moment, the actual capacity is calculated based on the preset new battery capacity decay degree. The preset threshold can be set as needed, for example, set to 80%.

[0183] Furthermore, in one embodiment, the life loss degree L of the battery at the current sampling time loss (t) can be calculated using the following method:

[0184] L loss (t)=|F[S oc (t)]-F[S oc (t-1)]|

[0185]

[0186] Among them, N life (.) is the fitting function of the relationship between the number of battery cycles and the depth of discharge DOD, and Soc(t) is the state of charge of the battery at sampling time t.

[0187] It should be noted that the energy storage capacity configuration scheme of the charging station system taking into account the dynamic loss of the energy storage battery proposed in the embodiment of the present invention has the following advantages:

[0188] 1. Improve the accuracy of system economic analysis and charging and discharging control strategies throughout the life cycle of energy storage batteries.

[0189] 2. A mixed integer linear programming model for the capacity configuration of the charging station system was constructed, which can optimize the capacity and power of the energy storage system at the same time. The implementation method is more efficient and convenient, and the overall economic benefits of the charging station are improved.

[0190] 3. The entire complex mixed-integer nonlinear programming problem of charging station system capacity configuration is converted into a mixed-integer linear problem through a linearization method, so that it can be solved using a mature MILP solver, which greatly simplifies the difficulty of solving the charging station system capacity configuration problem and improves the efficiency and reliability of the model solution.

[0191] In addition, an embodiment of the present invention further provides a charging station system configuration device, wherein the charging station system includes a photovoltaic power generation system and an energy storage system. Figure 3 , the charging station system configuration device comprises:

[0192] A first acquisition module 10 is used to acquire planning parameters of the charging station system;

[0193] The second acquisition module 20 is used to obtain the estimated load power and the estimated light intensity at each sampling moment;

[0194] A solution module 30, configured to solve a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints according to the planning parameters, the expected load power and the expected light intensity, wherein the target configuration scheme includes a target photovoltaic capacity of the photovoltaic power generation system and a target rated capacity and a target rated power of the energy storage system;

[0195] Among them, the preset constraints include at least a discharge power constraint and / or a charging power constraint of the energy storage system, the discharge power constraint includes a constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes a constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or battery charging rate at the sampling moment is calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

[0196] Furthermore, the planning parameters include power purchase target parameters, the system configuration target includes at least the power purchase target of the charging station system, the preset constraints also include a power purchase rate constraint of the charging station system, the power purchase rate constraint is used to constrain the power purchase rate of the charging station system, and is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the discharge power of the energy storage system and the discharge conditions at each sampling time, and the power purchase target is calculated based on the power purchase target parameters and the power purchase rate of the charging station system.

[0197] Furthermore, the solution module 30 is also used for:

[0198] Initialize a population and use the initialized population as a target population, and use each individual in the target population as a target individual, wherein an individual includes a photovoltaic capacity value of the photovoltaic power generation system, a rated capacity value and a rated power value of the energy storage system;

[0199] Solve the target charging and discharging scheme of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration scheme, wherein the target charging and discharging scheme specifies the discharge and charging conditions of the energy storage system at each sampling time;

[0200] Taking the system configuration target of the charging station system when the target individual is used as the configuration scheme and the energy storage system is charged and discharged according to the target charging and discharging scheme as the fitness value corresponding to the target individual;

[0201] Check whether the stop condition of the loop iteration is met;

[0202] If satisfied, then select the individual with the smallest fitness value from the target individuals as the target configuration scheme;

[0203] If not, individuals whose fitness values ​​meet the preset fitness conditions are selected from the target population as elite individuals, and the population composed of offspring individuals generated by crossover mutation and the elite individuals is used as the target population, and the operation of using each individual in the target population as the target individual is returned to be executed.

[0204] Furthermore, the system configuration target also includes an unabsorbed power generation target of the charging station system, the planning parameters also include unabsorbed power generation target parameters, the preset constraints also include unabsorbed power generation rate constraints and charging and discharging condition constraints of the energy storage system, the unabsorbed power generation rate constraints are used to constrain the unabsorbed power generation rate of the charging station system, which is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the charging power of the energy storage system and the charging condition at each sampling time, the charging and discharging condition constraints are used to constrain the energy storage system from being in the charging state and the discharging state at the same time, and the unabsorbed power generation target is calculated based on the unabsorbed power generation target parameters and the unabsorbed power generation rate.

[0205] Furthermore, the power purchase rate constraint condition is:

[0206]

[0207] The constraint condition of the power generation unabsorption rate is:

[0208]

[0209] The charging and discharging constraints are:

[0210] x t +y t ≤1

[0211] Among them, LOPE represents the electricity purchase rate, N represents the number of samples, and t l and t s represents the sampling time, P load (t) represents load power, P ESS (t) represents the charging or discharging power of the energy storage system, PRE (t) represents the power generation of the photovoltaic power station, x t is the variable representing the discharge condition, y t is a variable representing the charging status;

[0212] Furthermore, the solution module 30 is also used for:

[0213] After converting the nonlinear charging and discharging constraints into linear constraints, a mixed integer linear programming model solving algorithm is used to solve the target charging and discharging plan of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration plan.

[0214] Furthermore, the solution module 30 is also used to convert the nonlinear charging and discharging condition constraint condition into the following linear constraint condition:

[0215] m=x t ·P ESS (t l );

[0216] m≥x t ·P ESS (t l ) min ;

[0217] m≤x t ·P ESS (t l ) max ;

[0218] m≥P ESS (t l )-(1-x t )·P ESS (t l ) max ;

[0219] m≤P ESS (t l )-(1-x t )·P ESS (t l ) min ;

[0220] n=y t ·P RE (t s );

[0221] n≥y t ·P RE (t s ) min ;

[0222] n≤y t ·P RE (t s ) max ;

[0223] n≥P RE (t s )-(1-y t )·P RE (t s ) max ;

[0224] n≤P RE (t s )-(1-y t )·P RE (t s ) min .

[0225] Furthermore, the system configuration objectives also include construction and maintenance objectives of the energy storage system, the planning parameters also include the planned service life of the charging station system and the number of replacements of the energy storage system, the purchase coefficient of the energy storage capacity, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter and the maintenance coefficient per unit power, the construction and maintenance objectives include capacity maintenance objectives and power maintenance objectives, the capacity maintenance objective is calculated based on the number of replacements, the purchase coefficient of the energy storage capacity and the rated capacity of the energy storage system, and the power maintenance objective is calculated based on the number of replacements, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter, the maintenance coefficient per unit power, the planned service life and the rated power of the energy storage system.

[0226] Furthermore, the charging station system configuration device also includes:

[0227] a third acquisition module, for acquiring the capacity attenuation coefficient of the battery of the energy storage system at each stage as the battery capacity attenuation characteristic information, so as to calculate the actual capacity at the sampling moment by determining the target capacity attenuation coefficient corresponding to the stage of the battery at the current sampling moment, and calculating the capacity attenuation degree of the battery at the current sampling moment according to the target capacity attenuation coefficient, the current life loss degree of the battery and the capacity attenuation degree of the battery at the previous sampling moment, and calculating the actual capacity of the battery at the current sampling moment according to the capacity attenuation degree of the battery at the current sampling moment and the rated capacity of the energy storage system.

[0228] Further, when calculating the actual capacity at the sampling moment, when the stage of the battery at the current sampling moment is different from the stage of the battery at the previous sampling moment, the initial average capacity attenuation degree corresponding to the stage of the battery at the current sampling moment is obtained as the capacity attenuation degree of the battery at the previous sampling moment, so as to calculate the actual capacity at the current sampling moment;

[0229] When the battery is a new battery at the current sampling moment, the preset new battery capacity attenuation degree is used as the capacity attenuation degree of the battery at the previous sampling moment to calculate the actual capacity at the current sampling moment.

[0230] Further, when calculating the actual capacity at the sampling moment, when the calculated capacity attenuation degree at the current sampling moment is less than a preset threshold, it is determined that the battery is replaced with a new battery at the next sampling moment.

[0231] The extended contents of the specific implementation manner of the charging station system configuration device of the present invention are basically the same as the above-mentioned embodiments of the charging station system configuration method, and will not be elaborated here.

[0232] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a charging station system configuration program is stored. When the charging station system configuration program is executed by a processor, the steps of the charging station system configuration method described below are implemented.

[0233] The various embodiments of the charging station system configuration device and the computer-readable storage medium of the present invention may refer to the various embodiments of the charging station system configuration method of the present invention, and will not be described in detail here.

[0234] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0235] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0236] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0237] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A charging station system configuration method, characterized in that: The charging station system includes a photovoltaic power generation system and an energy storage system, and the charging station system configuration method includes the following steps: Acquiring planning parameters of the charging station system; Obtain the estimated load power and estimated light intensity at each sampling moment; According to the planning parameters, the expected load power and the expected light intensity, solving a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints, the target configuration scheme including the target photovoltaic capacity of the photovoltaic power generation system and the target rated capacity and target rated power of the energy storage system; Among them, the preset constraints include at least a discharge power constraint and / or a charging power constraint of the energy storage system, the discharge power constraint includes a constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes a constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or battery charging rate at the sampling moment is calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

2. The method for configuring a charging station system according to claim 1, wherein: The planning parameters include power purchase target parameters, the system configuration target includes at least the power purchase target of the charging station system, the preset constraints also include the power purchase rate constraints of the charging station system, the power purchase rate constraints are used to constrain the power purchase rate of the charging station system, and are calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the discharge power of the energy storage system and the discharge conditions at each sampling time, and the power purchase target is calculated based on the power purchase target parameters and the power purchase rate of the charging station system.

3. The charging station system configuration method according to claim 2, characterized in that: The step of solving the target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies the preset constraint conditions comprises: Initialize a population and use the initialized population as a target population, and use each individual in the target population as a target individual, wherein an individual includes a photovoltaic capacity value of the photovoltaic power generation system, a rated capacity value and a rated power value of the energy storage system; Solve the target charging and discharging scheme of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration scheme, wherein the target charging and discharging scheme specifies the discharge and charging conditions of the energy storage system at each sampling time; Taking the system configuration target of the charging station system when the target individual is used as the configuration scheme and the energy storage system is charged and discharged according to the target charging and discharging scheme as the fitness value corresponding to the target individual; Check whether the stop condition of the loop iteration is met; If satisfied, then select the individual with the smallest fitness value from the target individuals as the target configuration scheme; If not, individuals whose fitness values ​​meet the preset fitness conditions are selected from the target population as elite individuals, and the population composed of offspring individuals generated by crossover mutation and the elite individuals is used as the target population, and the process returns to execute the step of taking each individual in the target population as the target individual.

4. The method for configuring a charging station system according to claim 3, wherein: The system configuration target also includes an unabsorbed power generation target for the charging station system, the planning parameters also include unabsorbed power generation target parameters, the preset constraints also include unabsorbed power generation rate constraints and charging and discharging condition constraints for the energy storage system, the unabsorbed power generation rate constraints are used to constrain the unabsorbed power generation rate of the charging station system, which is calculated based on the expected load power, the expected light intensity, the photovoltaic capacity of the photovoltaic power generation system, the charging power of the energy storage system and the charging condition at each sampling time, the charging and discharging condition constraints are used to constrain the energy storage system from being in the charging state and the discharging state at the same time, and the unabsorbed power generation target is calculated based on the unabsorbed power generation target parameters and the unabsorbed power generation rate.

5. The method for configuring a charging station system according to claim 4, wherein: The step of solving the target charging and discharging scheme of the energy storage system that makes the system configuration target of the charging station system meet the preset target requirements and satisfy the preset constraint conditions when the target individual is used as the configuration scheme comprises: After converting the nonlinear charging and discharging constraints into linear constraints, a mixed integer linear programming model solving algorithm is used to solve the target charging and discharging plan of the energy storage system so that the system configuration target of the charging station system meets the preset target requirements and satisfies the preset constraints when the target individual is used as the configuration plan.

6. The method for configuring a charging station system according to claim 1, wherein: The system configuration objectives also include the construction and maintenance objectives of the energy storage system. The planning parameters also include the planned service life of the charging station system and the number of replacements of the energy storage system, the purchase coefficient of the energy storage capacity, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter and the maintenance coefficient per unit power. The construction and maintenance objectives include capacity maintenance objectives and power maintenance objectives. The capacity maintenance objective is calculated based on the number of replacements, the purchase coefficient of the energy storage capacity and the rated capacity of the energy storage system. The power maintenance objective is calculated based on the number of replacements, the purchase coefficient per unit power, the unit coefficient of the energy storage inverter, the maintenance coefficient per unit power, the planned service life and the rated power of the energy storage system.

7. The method for configuring a charging station system according to any one of claims 1 to 6, characterized in that: The charging station system configuration method further includes: The capacity attenuation coefficient of the battery of the energy storage system at each stage is obtained as the battery capacity attenuation characteristic information, so that when calculating the actual capacity at the sampling moment, the target capacity attenuation coefficient corresponding to the stage of the battery at the current sampling moment is determined, and the capacity attenuation degree of the battery at the current sampling moment is calculated according to the target capacity attenuation coefficient, the current life loss degree of the battery, and the capacity attenuation degree of the battery at the previous sampling moment, and the actual capacity of the battery at the current sampling moment is calculated according to the capacity attenuation degree of the battery at the current sampling moment and the rated capacity of the energy storage system.

8. The method for configuring a charging station system according to claim 7, wherein: When calculating the actual capacity at the sampling moment, when the stage of the battery at the current sampling moment is different from the stage of the battery at the previous sampling moment, the initial average capacity attenuation degree corresponding to the stage of the battery at the current sampling moment is obtained as the capacity attenuation degree of the battery at the previous sampling moment, so as to calculate the actual capacity at the current sampling moment; When the battery is a new battery at the current sampling moment, the preset new battery capacity attenuation degree is used as the capacity attenuation degree of the battery at the previous sampling moment to calculate the actual capacity at the current sampling moment.

9. The method for configuring a charging station system according to claim 8, wherein: When calculating the actual capacity at the sampling moment, when the calculated capacity attenuation degree at the current sampling moment is less than a preset threshold, it is determined that the battery is replaced with a new battery at the next sampling moment.

10. A charging station system configuration device, characterized in that: The charging station system includes a photovoltaic power generation system and an energy storage system, and the charging station system configuration device includes: A first acquisition module, used to acquire planning parameters of the charging station system; The second acquisition module is used to obtain the estimated load power and the estimated light intensity at each sampling moment; a solution module, configured to solve a target configuration scheme of the charging station system so that the system configuration target of the charging station system satisfies preset constraints according to the planning parameters, the expected load power and the expected light intensity, wherein the target configuration scheme includes a target photovoltaic capacity of the photovoltaic power generation system and a target rated capacity and a target rated power of the energy storage system; Among them, the preset constraints include at least a discharge power constraint and / or a charging power constraint of the energy storage system, the discharge power constraint includes a constraint consisting of the rated power of the energy storage system and the battery discharge rate, the charging power constraint includes a constraint consisting of the rated power of the energy storage system and the battery charging rate, the battery discharge rate and / or battery charging rate at the sampling moment is calculated based on the actual capacity of the energy storage system at the sampling moment, and the actual capacity at the sampling moment is calculated based on the rated capacity of the energy storage system and the battery capacity attenuation characteristic information.

11. A charging station system configuration device, characterized in that: The charging station system configuration device includes: a memory, a processor, and a charging station system configuration program stored in the memory and executable on the processor. When the charging station system configuration program is executed by the processor, the steps of the charging station system configuration method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a charging station system configuration program, and when the charging station system configuration program is executed by a processor, the steps of the charging station system configuration method according to any one of claims 1 to 9 are implemented.

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