Charging station configuration method, device, equipment and storage medium

Abstract

This disclosure relates to a method, apparatus, device, and storage medium for configuring a charging station. The method includes: for each vehicle arrival time within the charging station's operating cycle, based on a pre-built satisfaction model for charging time and price, determining the most satisfactory charging time information corresponding to the target user type at each vehicle arrival time, according to the vehicle arrival time and time-of-use electricity price; for each vehicle arrival time, determining the vehicle departure time based on the arrival time, the corresponding most satisfactory charging time information, and the departure time, determining the upper and lower bounds of the vehicle load power for each vehicle arrival time; obtaining the upper and lower bounds of the charging station load power using Minkowski summation based on the upper and lower bounds of the vehicle load power for each vehicle arrival time; and solving the charging station configuration strategy based on a pre-built comprehensive benefit model of the charging station and the upper and lower bounds of the charging station load power.

Description

Technical Field

[0001] This disclosure relates to the field of charging station technology, and in particular to a method, apparatus, device and storage medium for configuring a charging station. Background Technology

[0002] Electric vehicles, as a green and clean mode of transportation and a superior alternative to gasoline-powered vehicles, are gradually becoming a new development trend in the automotive industry. With the increasing number of electric vehicles on the road, the demand for electric vehicle charging stations is also constantly rising. However, the current operation of electric vehicle charging stations still faces pain points such as low overall user response and low utilization rate of charging piles, which directly affect the economic benefits of actual charging station operation. Therefore, how to optimize the configuration of charging stations to improve overall efficiency has become an urgent technical problem to be solved. Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, the present disclosure provides a method, apparatus, device and storage medium for configuring a charging station.

[0004] A first aspect of this disclosure provides a method for configuring a charging station, the method comprising:

[0005] For each vehicle arrival time within the operation cycle of a charging station, based on a pre-built satisfaction model for charging time and charging price, the most satisfactory charging time information corresponding to the target user type for the vehicle arrival time is determined according to the vehicle arrival time and the time-of-use electricity price. Among them, different user types have different sensitivities to charging time and charging price.

[0006] For each vehicle arrival time, the vehicle departure time is determined based on the vehicle arrival time. Based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined. Based on the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time, the upper and lower bounds of the charging station load power are obtained by using Minkowski summation.

[0007] Based on a pre-built comprehensive benefit model of the charging station, the configuration strategy of the charging station is solved according to the upper and lower bounds of the charging station's load power.

[0008] A second aspect of this disclosure provides a configuration apparatus for a charging station, the apparatus comprising:

[0009] The first determining module is used to determine the most satisfactory charging time information corresponding to the target user type for each vehicle arrival time within the operation cycle of the charging station, based on a pre-built satisfaction model for charging time and charging price, and according to the vehicle arrival time and time-of-use electricity price. Different user types have different sensitivities to charging time and charging price.

[0010] The second determining module is used to determine the vehicle departure time for each vehicle arrival time, and to determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time. The upper and lower bounds of the charging station load power are obtained by using Minkowski summation based on the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time.

[0011] The solution module is used to solve the configuration strategy of the charging station based on a pre-built comprehensive benefit model of the charging station and according to the upper and lower bounds of the charging station's load power.

[0012] A third aspect of this disclosure provides an electronic device, the server comprising: a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the method of the first aspect described above.

[0013] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the method of the first aspect described above.

[0014] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0015] This embodiment of the disclosure can optimize the configuration of charging stations by analyzing user charging response characteristics (i.e., the most satisfactory charging time information corresponding to each preset user type), thereby improving the rationality of charging station configuration, increasing the utilization rate of charging station equipment, and enhancing the scheduling potential of charging stations, thus improving overall comprehensive benefits. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a charging station configuration method provided in an embodiment of this disclosure;

[0019] Figure 2 This is a schematic diagram of the upper and lower limits of vehicle load power provided in an embodiment of this disclosure;

[0020] Figure 3 This is a flowchart of another method for configuring a charging station provided in an embodiment of this disclosure;

[0021] Figure 4 This is a schematic diagram of the structure of a charging station equipment optimization configuration analysis system provided in an embodiment of this disclosure;

[0022] Figure 5 This is a schematic diagram of the input and output of each module in a charging station equipment optimization configuration analysis system provided in this embodiment of the disclosure;

[0023] Figure 6 This is a flowchart of a method for solving the adjustable boundary of a charging station load, provided in an embodiment of this disclosure;

[0024] Figure 7 This is a schematic diagram of the configuration device for a charging station provided in an embodiment of this disclosure;

[0025] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. Detailed Implementation

[0026] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0027] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0028] As mentioned earlier, with the increasing number of electric vehicles, the demand for electric vehicle charging stations is also constantly increasing. However, the operation of electric vehicle charging stations still faces pain points such as low overall user response and low utilization rate of charging piles, which directly affects the economic benefits of actual charging station operation. Meanwhile, with the continuous development of my country's power market, charging stations or charging station clusters can participate as third-party entities in power system peak-shaving ancillary services to obtain additional revenue. However, the regulation capacity of charging stations participating in peak-shaving ancillary services is affected by the charging response characteristics of electric vehicle users, and these user response characteristics are complex. Furthermore, electric vehicle charging loads are flexible. If the regulation capacity of large-scale electric vehicle charging loads can be fully utilized, it will provide operators with more diversified profit and control methods, such as participating in the peak-shaving ancillary service market to obtain additional revenue. However, as large-scale electric vehicle charging load clusters, electric vehicle charging stations currently face problems such as low overall response, low utilization rate of charging piles, and high capacity of distribution transformers, resulting in the need to improve the operating revenue of charging stations. Therefore, under the aforementioned background, how to improve the overall comprehensive benefits of charging stations based on their actual conditions, through participation in peak-shaving ancillary services and other revenue-generating methods, as well as the upgrading of charging station configurations, is a pressing issue that needs to be addressed in the optimization and operation strategy of charging stations. Regarding the response volume of electric vehicle users, current research focuses primarily on the characteristics of electric vehicle users' response to electricity demand. For the planning or optimization of electric vehicle charging stations, existing research mainly focuses on site selection and capacity allocation with the goal of maximizing charging demand. In the electricity market, research on the optimization of charging station equipment configuration that simultaneously considers electric vehicle charging demand, power system peak shaving, and the response characteristics of electric vehicle users has not yet become a focus of researchers. This disclosure proposes, in the aforementioned context, a method for optimizing the configuration of charging stations that considers the user response characteristics of electric vehicles participating in the peak-shaving ancillary service market. By analyzing the response characteristics of electric vehicle users, the method optimizes the configuration of charging stations, improves the rationality of charging station configuration, thereby increasing the utilization rate of charging station equipment and enhancing the dispatchability potential of charging stations, thus improving overall comprehensive benefits.

[0029] Figure 1 This is a flowchart illustrating a method for configuring a charging station according to an embodiment of this disclosure. This method can be executed by an electronic device. The electronic device can be exemplarily understood as a device such as a mobile phone, tablet computer, laptop computer, desktop computer, or smart TV. Figure 1 As shown, the method provided in this embodiment includes the following steps.

[0030] S110. For each vehicle arrival time within the operation cycle of the charging station, based on a pre-built satisfaction model for charging time and charging price, determine the most satisfactory charging time information corresponding to the target user type at the vehicle arrival time according to the vehicle arrival time and time-of-use electricity price. Among them, different user types have different sensitivities to charging time and charging price.

[0031] Specifically, the operating cycle refers to the operating time of a charging station during the day.

[0032] Specifically, the vehicle arrival time is the time when the vehicle arrives at the charging station. Multiple vehicle arrival times can be selected evenly within the operating period, or multiple vehicle arrival times can be selected randomly within the operating period. This disclosure does not limit this selection.

[0033] Specifically, time-of-use pricing refers to the charging price at each moment within the operating cycle.

[0034] Specifically, the target user type can be randomly selected from multiple user types, where different user types have different sensitivities to charging time and charging price.

[0035] For example, multiple user types and the number of users corresponding to each user type can be obtained through a questionnaire survey, as shown in Table 1. The number of users for each user type should satisfy the following formula (1):

[0036] Formula (1)

[0037] Table 1

[0038]

[0039] Specifically, a user satisfaction model for charging time and charging price can be built based on Weber-Fechner's law, but it is not limited to this.

[0040] Optionally, based on a pre-built satisfaction model for charging time and charging price, the most satisfactory charging time information corresponding to the target user type at the vehicle arrival time is determined according to the vehicle arrival time and time-of-use electricity price, including:

[0041] Randomly select the target user type corresponding to the vehicle's arrival time from multiple user types;

[0042] Obtain the first weight value for charging time and the second weight value for charging price for the target user type;

[0043] Based on the vehicle arrival time, time-of-use electricity price, vehicle charging time, first weight value, and second weight value, the satisfaction of the target user type under different charging periods is determined, and the most satisfactory charging time information is determined according to the charging period corresponding to the highest satisfaction. Among them, the vehicle charging time is calculated based on the vehicle's initial SOC and the preset charging power. The vehicle's initial SOC is obtained based on normal distribution sampling.

[0044] Specifically, the first weight value and the second weight value corresponding to each user type can be set by those skilled in the art according to the actual situation, and are not limited here. For example, the first weight value corresponding to each user type (denoted as...) ) and the second weight value (denoted as (as shown in Table 2).

[0045] Table 2

[0046]

[0047] In some embodiments, the satisfaction model is a user satisfaction model that takes into account the load shift of electric vehicles, and the most satisfactory charging time information includes the most satisfactory start charging time.

[0048] Specifically, based on vehicle arrival time, time-of-use electricity price, vehicle charging time, first weight value, and second weight value, the satisfaction level of target user types under different charging periods is determined. Furthermore, the most satisfactory charging time information is determined based on the charging period corresponding to the highest satisfaction level, including:

[0049] For each preset extension period, the satisfaction level of the target user type under the charging period corresponding to the preset extension period is determined based on the first charging price, the second charging price, the vehicle charging time, the extension duration corresponding to the preset extension period, the first weight value, and the second weight value. The first charging price is the charging price from the moment the vehicle arrives at the station until it is fully charged, the second charging price is the charging price for the charging period corresponding to the preset extension period, and the extension duration is the product of the preset extension period and the unit duration.

[0050] The preset extension period corresponding to the highest satisfaction level is taken as the target extension period.

[0051] The optimal start time for charging is obtained by extending the vehicle's arrival time backward by the target extension period.

[0052] Specifically, the preset extension period is an integer. It should be noted that the specific range of the preset extension period and the specific duration of the unit duration can be set by those skilled in the art according to actual circumstances, and are not limited here. For example, the minimum value of the preset extension period is 0 and the maximum value is [missing value]. The unit duration is , and The product can take any value within the range of 2 to 3 hours, but is not limited to this.

[0053] Specifically, the first charging price is the charging price for the current charging period. The start time of the current charging period is the vehicle's arrival time, and the end time of the current charging period is the time when the vehicle is fully charged according to the preset charging power, starting from the vehicle's arrival time. In other words, the vehicle's arrival time is postponed by the time the vehicle's charging time is delayed.

[0054] Specifically, the second charging price is the charging price for the extended charging period, which is the period obtained by delaying the current charging period by an extended time.

[0055] Specifically, the charging period corresponding to the preset number of extension periods is the extension charging period.

[0056] Specifically, according to Weber's Law, the difference in intensity of the same stimulus must reach a certain proportion to elicit a difference sensation. This proportion is a constant, expressed by the formula: ΔI (difference threshold) / I (standard stimulus intensity) = k (constant / Weber fraction). The minimum perceptible difference (continuous difference threshold) is taken as the unit of the sensory quantity, that is, for each increase of the difference threshold, the psychological quantity increases by one unit. The sensory quantity is directly proportional to the logarithm of the physical quantity, that is, the increase in sensory quantity lags behind the increase in physical quantity. The physical quantity grows geometrically, while the psychological quantity grows arithmetically, as shown in formula (2):

[0057] Formula (2)

[0058] in, For perceived intensity, Stimulus intensity It is a constant, that is, the weight value of each type of stimulus.

[0059] Based on this, the user satisfaction model considering the load shift of electric vehicles includes the following formulas (3)-(6):

[0060] Formula (3)

[0061] Formula (4)

[0062] Formula (5)

[0063] Formula (6)

[0064] in, The impact of charging prices and charging time on electric vehicle users. As the first weight value, As the second weight value, For the first charging price, For the second charging price, The charging time for a vehicle is measured by the initial State of Charge (SOC). For electric vehicle capacity, The charging power of the charging station To preset the number of extension periods, The duration is expressed in units of time.

[0065] Formula (5) represents the goal of maximizing satisfaction, and Formula (6) is a preset delay period constraint, meaning that the delay cannot be unlimited. Based on the preset delay period with the minimum stimulus level, the user's response characteristics to immediate charging and delayed charging can be analyzed. If the target delay period corresponding to the minimum stimulus level is 0, the user chooses immediate charging; if the target delay period corresponding to the minimum stimulus level is not 0, the user is willing to delay charging for a target delay period of several units.

[0066] In other embodiments, the satisfaction model is a user satisfaction model that takes into account the charging power control of electric vehicles, and the most satisfactory charging time information includes the target charging delay.

[0067] Specifically, based on vehicle arrival time, time-of-use electricity price, vehicle charging time, first weight value, and second weight value, the satisfaction level of target user types under different charging periods is determined. Furthermore, the most satisfactory charging time information is determined based on the charging period corresponding to the highest satisfaction level, including:

[0068] For each preset power reduction, the satisfaction of the target user type during the charging period corresponding to the preset power reduction is determined based on the first charging price, the compensation price corresponding to the preset power reduction, the vehicle charging time, the power reduction charging delay corresponding to the preset power reduction, the first weight value, and the second weight value. The first charging price is the charging price from the moment the vehicle arrives at the station until it is fully charged. The compensation price is the price compensated when the charging power is reduced by the preset power reduction. The power reduction charging delay is the extended charging time caused by the reduction in the preset power reduction.

[0069] The preset power reduction corresponding to the maximum satisfaction level is taken as the target power reduction.

[0070] The power reduction charging delay corresponding to the target power reduction is taken as the target charging delay.

[0071] Specifically, the preset power reduction is the power value reduced from the preset charging power. It should be noted that the specific range of the preset power reduction can be set by those skilled in the art according to actual conditions, and is not limited here.

[0072] Specifically, the preset charging period corresponding to the reduced power starts at the time the vehicle arrives at the station and ends at the time when the vehicle is fully charged according to the reduced power charging power. The reduced power charging power is the difference between the preset charging power and the preset charging power.

[0073] Specifically, the user satisfaction model for electric vehicle charging power control includes the following formulas (7)-(6):

[0074] Formula (7)

[0075] Formula (8)

[0076] Formula (9)

[0077] Formula (10)

[0078] The impact of charging prices and charging time on electric vehicle users. As the first weight value, As the second weight value, For the first charging price, for The corresponding compensation electricity price (i.e., the compensation electricity price for users after controlling the charging power). The charging time for a vehicle is measured by the initial State of Charge (SOC). For electric vehicle capacity, The charging power of the charging station To reduce power by default, This refers to the charging delay (i.e., the extended charging time caused by charging power control).

[0079] S120. For each vehicle's arrival time, determine the vehicle's departure time based on the arrival time. Based on the vehicle's arrival time, the most satisfactory charging time information corresponding to the arrival time, and the departure time, determine the upper and lower bounds of the vehicle's load power corresponding to each vehicle's arrival time. Based on the upper and lower bounds of the vehicle's load power corresponding to each vehicle's arrival time, use Minkowski summation to obtain the upper and lower bounds of the charging station's load power.

[0080] Specifically, because charging prices and service fees vary at different times, some electric vehicle users may choose longer charging wait times to pursue lower charging prices; the charging load generated by these users is adjustable load. Conversely, other electric vehicle users may choose higher charging prices to pursue shorter charging wait times; the charging load generated by these users is non-adjustable load. To obtain the overall adjustable boundary of the charging station load, it is necessary to analyze the adjustable and non-adjustable loads at different times within an operating cycle.

[0081] Specifically, the vehicle departure time is the time after which the vehicle arrival time is delayed by an expected duration. The expected duration can be set by those skilled in the art according to the actual situation, and is not limited here. For example, the expected duration is 4 hours or 5 hours, but it is not limited to this.

[0082] Specifically, the upper and lower limits of vehicle load power are a parallelogram, which usually refers to the range of limits that the battery management system (BMS) sets on charging and discharging power during the charging process of an electric vehicle.

[0083] In some embodiments, the optimal charging time information includes the optimal start charging time. In this case, the vehicle departure time is determined based on the vehicle arrival time, and the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined based on the vehicle arrival time, the optimal charging time information corresponding to the vehicle arrival time, and the vehicle departure time, including:

[0084] By working backwards from the time the vehicle left the station, the charging time of the vehicle can be calculated to obtain the latest time when the vehicle started charging.

[0085] Based on the most satisfactory start time for charging, the latest start time for charging, and the departure time of the vehicle corresponding to the target user type, the upper and lower bounds of the vehicle load power are obtained.

[0086] For example, Figure 2 This is a schematic diagram illustrating the upper and lower limits of vehicle load power provided in an embodiment of this disclosure. Figure 2 As shown, the horizontal axis represents time, and the vertical axis represents battery power (10 represents a full charge). The parallelogram formed by the most satisfactory start time of charging, the latest start time of charging, and the time the vehicle leaves the station represents the upper and lower limits of the vehicle's load power.

[0087] In other embodiments, the optimal charging time information includes the target charging delay. In this case, the vehicle departure time is determined based on the vehicle arrival time, and the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined based on the vehicle arrival time, the optimal charging time information corresponding to the vehicle arrival time, and the vehicle departure time, including:

[0088] The sum of the vehicle charging time and the target charging delay corresponding to the target user type is taken as the new vehicle charging time.

[0089] By working backwards from the vehicle's departure time, the latest time when charging begins with reduced power is obtained;

[0090] The upper and lower limits of the vehicle load power are obtained based on the vehicle's arrival time, the latest start time of power reduction charging, and the vehicle's departure time.

[0091] For example, such as Figure 2 As shown, the parallelogram formed by the vehicle's arrival time, the latest start time of power reduction charging, and the vehicle's departure time is the upper and lower bounds of the vehicle's load power.

[0092] Specifically, by using the Minkowski summation, the upper and lower bounds of the vehicle load power corresponding to the departure time of each vehicle within the operating cycle are aggregated into the upper and lower bounds of the electric vehicle cluster load power, thereby obtaining the upper and lower bounds of the charging station load power.

[0093] The Minkowski summation is shown in formula (11):

[0094] Formula (11)

[0095] The Minkowski summation of A and B results in a new set (i.e., ... Each element in this set is the result of adding a point a in A and a point b in B.

[0096] S130. Based on the pre-built comprehensive benefit model of the charging station, solve the configuration strategy of the charging station according to the upper and lower bounds of the charging station load power.

[0097] Specifically, based on the upper and lower bounds of the charging station load power, considering the revenue of the ancillary service market and taking into account various constraints, a comprehensive benefit model for the charging station is constructed. Then, the specific values ​​of the upper and lower bounds of the charging station load power obtained in S120 are substituted into the comprehensive benefit model of the power station to solve for the configuration strategy of the charging station.

[0098] For a charging station, its revenue consists of ancillary service market revenue, user charging revenue, electricity purchase cost, energy storage cost, and charging station facility configuration cost. The capacity of its ancillary service market is determined by factors such as user responsiveness, the number of charging piles, energy storage capacity configuration, and distribution transformer capacity configuration. Therefore, the following three scenarios can be studied separately:

[0099] Scenario 1: For existing battery swapping stations, introduce charging piles. Given the existing distribution transformer capacity and energy storage capacity, construct a comprehensive benefit model for the charging station using the number of charging piles introduced as the optimization variable.

[0100] Scenario 2: Introducing energy storage into existing charging stations. Given the existing distribution transformer capacity and the number of charging piles, a comprehensive benefit model for the charging station is constructed using the energy storage capacity configuration as the optimization variable.

[0101] Scenario 3: For a charging station to be built, given the number and configuration of charging piles, construct a comprehensive benefit model for the charging station using the configuration of distribution transformer capacity and energy storage capacity as optimization variables.

[0102] Optionally, the comprehensive benefit model of a charging station includes constraints on charging piles within the charging station, energy storage within the charging station, capacity constraints on distribution transformers within the charging station, peak shaving auxiliary service constraints, valley filling auxiliary service constraints, and comprehensive costs.

[0103] S130 includes: For existing battery swapping stations, based on the upper and lower limits of the charging station load power, the capacity configuration of the distribution transformer, and the energy storage capacity configuration, to solve the configuration of the number of charging piles that minimizes the overall cost;

[0104] For existing charging stations, based on the upper and lower limits of the charging station load power, the capacity configuration of the distribution transformer, and the number of charging piles, we can solve for the energy storage capacity configuration that minimizes the overall cost.

[0105] For the charging station to be built, based on the upper and lower limits of the charging station's load power and the configuration of the number of charging piles, we can solve for the distribution transformer capacity configuration and energy storage capacity configuration that minimize the overall cost.

[0106] 1. Charging pile constraints within the charging station

[0107] Formula (12)

[0108] Formula (13)

[0109] in, and These are the lower and upper limits of the charging station load, respectively. The load power of the charging station after load shifting. The charging station load power before load shifting. For one operating cycle. Formula (12) limits the total charging power of all charging piles in the charging station to the adjustable boundary of the charging load of the charging station (i.e., the upper and lower limits of the charging load power of the charging station); Formula (13) ensures that the total amount of electric vehicle charging in a day remains unchanged after the charging load is shifted during the scheduling process.

[0110] Formula (14)

[0111] in, This represents the maximum power output of the charging station. The number of charging stations. The maximum number of charging piles can be configured. Formula (14) limits the total charging power of all charging piles in the charging station to the maximum total charging power.

[0112] Formula (15)

[0113] in, This refers to the power output of the charging station transformer. The charging power for energy storage. Formula (15) represents the time period. Power balance within the charging station.

[0114] 2. Energy storage constraints within charging stations

[0115] Formula (16)

[0116] Formula (17)

[0117] Formula (18)

[0118] Formula (19)

[0119] in, For energy storage charging power, This refers to the discharge rate of the energy storage, which assumes that the charging and discharging power of the energy storage is proportional to its capacity. This is the corresponding proportionality coefficient. Configured for energy storage capacity, To store the energy at time t+1, For time period Energy storage capacity, For energy storage charging efficiency, For a unit of time, The initial energy storage capacity at the start of dispatch. This is the ratio of the initial energy storage capacity to the total capacity, i.e., the percentage of initial energy storage. and These are the lower and upper bounds of the energy storage capacity, respectively. Formula (16) is the energy storage charging power constraint, formula (17) is the energy storage capacity balance constraint, formula (18) is the initial energy storage capacity constraint, and formula (19) is the energy storage capacity safety constraint.

[0120] 3. Capacity constraints of distribution transformers within charging stations

[0121] Formula (20)

[0122] in, For internet access power, For the power output of the network, The power purchased by the charging station is given by formula (20), which is the power constraint for the charging station to connect to the grid and disconnect from the grid.

[0123] 4. Constraints on Peak Shaving Ancillary Services for Charging Stations

[0124] Formula (21)

[0125] Formula (22)

[0126] Formula (23)

[0127] in, The target power regulation that charging stations need to achieve to participate in the peak shaving ancillary service market. For a sufficiently large constant, the peak-shaving auxiliary power supply of the charging station, As the first Boolean variable, For peak shaving auxiliary service time periods, Peak power reduction for charging stations. Formula (21) ensures that the configured energy storage capacity meets the regulation power access conditions for charging stations to participate in the ancillary services market.

[0128] 5. Constraints on charging station valley-filling ancillary services

[0129] Formula (24)

[0130] Formula (25)

[0131] Formula (26)

[0132] in, The target regulation power that charging stations need to achieve to participate in the valley-filling ancillary service market. For a sufficiently large constant, the off-peak auxiliary power supply of charging stations, As the second Boolean variable, For the collection of valley filling auxiliary service periods, The energy storage capacity is used to fill off-peak electricity for the charging station. Formula (24) ensures that the configured energy storage capacity meets the power access conditions for the charging station to participate in the ancillary services market.

[0133] 6. Determine the objective function. The objective function is the same for different scenarios: minimizing the overall cost.

[0134] The objective function is as follows:

[0135] Formula (27)

[0136] Formula (28)

[0137] Formula (29)

[0138] Formula (30)

[0139] Formula (31)

[0140] Formula (32)

[0141] Formula (33)

[0142] Where n represents the number of operating years. For the maximum operating life, , and These are the costs for energy storage capacity configuration, charging pile quantity configuration, and distribution transformer configuration, respectively. For the capacity of the distribution transformer, This refers to the unit price of energy storage loss. For energy storage charging efficiency, For energy storage charging power, For a unit of time, The unit price of electricity purchased from the power grid. Purchase power for charging stations and These are the unit prices of revenue in the ancillary services market. To reduce peak power consumption at charging stations. Fill in the off-peak electricity for charging stations. Electricity price for electric vehicle users, This refers to the unit price of charging pile losses. This is the maximum charging power of the charging station. arrive The costs include: purchase cost of charging piles and energy storage equipment, energy storage loss cost, electricity purchase cost from the energy market, revenue from ancillary services for peak shaving and valley filling, revenue from electric vehicle charging, and charging pile loss cost. Given the total cost, the optimization objective is to minimize the total cost.

[0143] For the three scenarios mentioned above, the known configurations and the unknown configurations to be solved differ in each scenario. For Scenario 1, the inputs to the comprehensive benefit model of the charging station are various electricity prices, energy storage capacity configuration, distribution transformer capacity configuration, and the upper and lower bounds of the charging station load power. The solution process is as follows: First, set the optimization variables; in Scenario 1, the optimization variable is the number of charging piles. Second, consider the constraints and determine the objective function. The output should be the specific value of the number of charging piles and the final total revenue. For Scenarios 2 and 3, the solution process is the same as Scenario 1, but the inputs for Scenario 2 are various electricity prices, the number of charging piles, distribution transformer capacity configuration, and the upper and lower bounds of the charging station load power, etc. The optimization variable is set as energy storage capacity configuration. The output should be the specific value of the energy storage capacity configuration and the final total revenue. For Scenario 3, the inputs are various electricity prices, the number of charging piles, and the upper and lower bounds of the charging station load power, etc. The optimization variables are set as distribution transformer capacity configuration and energy storage capacity configuration. The output should be the specific values ​​of the distribution transformer capacity configuration and energy storage capacity configuration, and the final total revenue.

[0144] This disclosure embodiment can construct a satisfaction model based on charging time and charging price as the main factors, accurately classify users according to their sensitivity to charging time and charging price, analyze the charging response characteristics of different types of users, and better reflect the psychology of electric vehicle users. Furthermore, it can calculate the adjustable and non-adjustable loads of the charging station for each time period based on the charging response characteristics of users in each time period within an operating cycle, and obtain the adjustable boundary of the charging station load (i.e., the upper and lower limits of the charging station load power), which is more realistic. Moreover, by constructing a comprehensive benefit model for the charging station, based on the upper and lower limits of the charging station load power and taking into account relevant constraints, it can better solve practical problems of charging stations in different scenarios, thereby improving the overall comprehensive benefit of the charging station.

[0145] The configuration method of the charging station provided in this embodiment will be described in detail below with reference to a specific example. Figure 3 This is a flowchart of another charging station configuration method provided in an embodiment of this disclosure. Figure 4 This is a schematic diagram of the structure of a charging station equipment optimization configuration analysis system provided in an embodiment of this disclosure. Figure 5 This is a schematic diagram of the input and output of each module in a charging station equipment optimization configuration analysis system provided in an embodiment of this disclosure. Figure 6 This is a flowchart illustrating how to solve for the adjustable boundary of a charging station load, as provided in an embodiment of this disclosure. Figure 3-6As shown, firstly, a user satisfaction model is constructed based on Weber-Fechner's law regarding both charging time and charging price. Using charging time and time-of-use pricing as inputs, the initial state of charge (SOC) of the vehicle is obtained based on normal distribution sampling. The response characteristics of electric vehicle users to instant charging and delayed charging are analyzed to obtain the charging decision behavior of electric vehicle users at charging stations at different times (i.e., user response characteristics). Specifically, the first step involves accurately classifying users during peak, off-peak, and normal times based on their sensitivity to charging time and price, assigning different weights to the two features of charging time and price for different user categories. The second step involves constructing a satisfaction model for charging time and price based on Weiper-Fechner's law; obtaining the vehicle's initial SOC at arrival time using normal distribution sampling, and calculating the time required for a user to fully charge (i.e., charging time) based on the initial SOC. The third step involves calculating the satisfaction of the same type of user at the same arrival time under different charging periods and time-of-use prices, based on the weights of the user's arrival time (i.e., vehicle arrival time), time-of-use pricing, vehicle charging time, and the charging time and price corresponding to the user type. The number of extended charging periods for this user type (i.e., the target extended charging period number) is calculated based on the principle of maximizing satisfaction, and the most satisfactory starting charging time (i.e., the user's most satisfactory charging time) is determined based on this target extended charging period number. In this way, for each type of electric vehicle within an operating cycle, the number of extended charging periods can be calculated, and the number of users willing to control charging power can be analyzed by determining whether this number is 0. For example, such as Figure 6 As shown, the operation cycle includes 24 time periods, each lasting 1 hour, with N vehicles arriving at the station during each time period. For each vehicle, user types are randomly selected to calculate the user's most satisfactory charging time for that vehicle.

[0146] Then, using the vehicle departure time as input, and combining the charging behavior decisions of electric vehicle users at different times, the upper and lower bounds of the vehicle load power at the charging station for each time period are calculated. Based on the Minkowski summation, the adjustable boundary of the charging station load (i.e., the upper and lower bounds of the charging station load power) is obtained. Specifically, the first step involves using the vehicle arrival time, vehicle departure time, and the most satisfactory start time of charging as input, firstly calculating the latest start time of charging based on the vehicle departure time and vehicle charging time; the second step involves combining the vehicle arrival time, vehicle departure time, and the latest start time of charging, taking the most satisfactory start time of charging as the user's charging start time, and analyzing the upper and lower bounds of the electric vehicle charging power load; the third step involves using the Minkowski summation to obtain the upper and lower bounds of the charging station load power.

[0147] Finally, based on the adjustable boundary of charging station load, considering the revenue of ancillary service market and taking into account various constraints, a comprehensive benefit model of charging station is constructed, and the overall optimal configuration strategy of charging station under different scenarios is obtained. Specifically, the first step involves modeling the comprehensive benefits of charging stations based on actual conditions and different scenarios. The optimal configuration of charging stations is divided into three scenarios: 1) For existing battery swapping stations, charging equipment is introduced, meaning the original distribution transformer capacity and energy storage capacity are known, and the number of charging piles introduced is used as the optimization variable; 2) For existing charging stations, energy storage is introduced, meaning the original distribution transformer capacity and the number of charging piles are known, and the energy storage capacity is used as the optimization variable; 3) For charging stations to be built, the number of charging piles is known, and the distribution transformer capacity and energy storage capacity are used as the optimization variables. The second step involves using the energy market electricity purchase price, user charging price, charging station facility capacity configuration, and charging station load characteristics as inputs to the comprehensive benefit model. The values ​​of the optimization variables for each scenario and the final total revenue are used as the model outputs. The objective function is to maximize the total revenue, taking into account various constraints, including load adjustability boundaries. The models for the three scenarios are then solved. This achieves the optimal configuration of charging station equipment.

[0148] Figure 7 This is a schematic diagram of the configuration device for a charging station provided in an embodiment of this disclosure. This configuration device can be understood as the aforementioned electronic device or a functional module within the aforementioned electronic device. Figure 7 As shown, the configuration device of the charging station includes:

[0149] The first determining module 710 is used to determine the most satisfactory charging time information corresponding to the target user type for each vehicle arrival time within the operation cycle of the charging station, based on a pre-built satisfaction model for charging time and charging price, and according to the vehicle arrival time and time-of-use electricity price. The user types have different sensitivities to charging time and charging price.

[0150] The second determining module 720 is used to determine the vehicle departure time based on the vehicle arrival time for each vehicle arrival time, and to determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and to obtain the upper and lower bounds of the charging station load power by using Minkowski summation based on the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time.

[0151] The solver module 730 is used to solve the configuration strategy of the charging station based on the pre-built comprehensive benefit model of the charging station and according to the upper and lower bounds of the charging station load power.

[0152] Optionally, the first determining module 710 includes a first determining submodule, used to determine the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time based on the pre-built satisfaction model for charging time and charging price, according to the vehicle arrival time and time-of-use electricity price. The first determining submodule includes:

[0153] An extraction submodule is used to randomly extract the target user type corresponding to the vehicle's arrival time from multiple user types;

[0154] The first acquisition submodule is used to acquire a first weight value for the target user type in relation to charging time and a second weight value in relation to charging price.

[0155] The first determining submodule is used to determine the satisfaction level of the target user type under different charging periods based on the vehicle arrival time, the time-of-use electricity price, the vehicle charging time, the first weight value, and the second weight value, and to determine the most satisfactory charging time information based on the charging period corresponding to the highest satisfaction level. The vehicle charging time is calculated based on the vehicle's initial SOC and the preset charging power. The vehicle's initial SOC is obtained based on normal distribution sampling.

[0156] Optionally, the most satisfactory charging time information includes the most satisfactory start time of charging;

[0157] The first determining submodule is specifically used to determine the satisfaction level of the target user type under the charging period corresponding to the preset number of delayed periods, based on the first charging price, the second charging price, the vehicle charging time, the delay duration corresponding to the preset number of delayed periods, the first weight value, and the second weight value, for each preset number of delayed periods. The first charging price is the charging price from the moment the vehicle arrives at the station until it is fully charged, the second charging price is the charging price for the charging period corresponding to the preset number of delayed periods, and the delay duration is the product of the preset number of delayed periods and the unit time.

[0158] The preset extension period corresponding to the highest satisfaction level is taken as the target extension period.

[0159] The optimal charging start time is obtained by extending the vehicle arrival time backward by the target extension period.

[0160] Optionally, the second determining module 720 includes a second determining submodule, used to determine the vehicle departure time based on the vehicle arrival time, and to determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time. Specifically, the second determining submodule is used to calculate the vehicle charging time backward from the vehicle departure time to obtain the latest start time of vehicle charging.

[0161] The upper and lower bounds of the vehicle load power are obtained based on the most satisfactory start time of charging, the latest start time of charging of the vehicle, and the departure time of the vehicle corresponding to the target user type.

[0162] Optionally, the most satisfactory charging time information includes the target charging delay;

[0163] The first determining submodule is specifically used to determine the satisfaction level of the target user type during the charging period corresponding to the preset reduced power, based on a first charging price, a compensation electricity price corresponding to the preset reduced power, the vehicle charging time, the power reduction charging delay corresponding to the preset reduced power, the first weight value, and the second weight value. The first charging price is the charging price from the moment the vehicle arrives at the station until it is fully charged. The compensation electricity price is the electricity price compensated when the charging power is reduced by the preset reduced power. The power reduction charging delay is the extended charging time caused by the reduction in the charging power of the preset reduced power.

[0164] The preset power reduction corresponding to the maximum satisfaction level is taken as the target power reduction.

[0165] The power reduction charging delay corresponding to the target power reduction is taken as the target charging delay.

[0166] Optionally, the second determining module 720 includes a second determining submodule, used to determine the vehicle departure time based on the vehicle arrival time, and to determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time. Specifically, the second determining submodule is used to take the sum of the vehicle charging time and the target charging delay corresponding to the target user type as the new vehicle charging time.

[0167] By subtracting the new vehicle charging time backward from the vehicle departure time, the latest charging start time with reduced power can be obtained;

[0168] The upper and lower bounds of the vehicle load power are obtained based on the vehicle's arrival time, the latest start time of the reduced power charging, and the vehicle's departure time.

[0169] Optionally, the comprehensive benefit model of the charging station includes constraints on charging piles within the charging station, constraints on energy storage within the charging station, constraints on the capacity of distribution transformers within the charging station, constraints on peak shaving auxiliary services of the charging station, constraints on valley filling auxiliary services of the charging station, and comprehensive costs.

[0170] The solution module 730 is specifically used to solve for the configuration of the number of charging piles that minimizes the overall cost for existing battery swapping stations, based on the upper and lower limits of the charging station's load power, the capacity configuration of the distribution transformer, and the energy storage capacity configuration.

[0171] For existing charging stations, based on the upper and lower limits of the charging station load power, the capacity configuration of the distribution transformer, and the number of charging piles, the energy storage capacity configuration corresponding to the minimum comprehensive cost is calculated.

[0172] For the charging station to be built, based on the upper and lower limits of the charging station's load power and the configuration of the number of charging piles, the corresponding distribution transformer capacity configuration and energy storage capacity configuration that minimize the overall cost are calculated.

[0173] The apparatus provided in this embodiment can execute the methods of any of the above embodiments, and its execution method and beneficial effects are similar, so they will not be described again here.

[0174] This disclosure also provides an electronic device, which includes: a memory storing a computer program; and a processor for executing the computer program, wherein when the computer program is executed by the processor, it can implement the methods of any of the above embodiments.

[0175] Example, Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. See below for details. Figure 8 The diagram illustrates a structural schematic suitable for implementing the electronic device 800 in the embodiments of this disclosure. The electronic device 800 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0176] like Figure 8As shown, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device 800. The processing device 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0177] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic device 800 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8 An electronic device 800 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0178] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a storage device 808, or installed from a ROM 802. When the computer program is executed by a processing device 801, it performs the functions defined in the methods of embodiments of this disclosure.

[0179] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0180] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0181] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0182] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: for each vehicle arrival time within the operating cycle of the charging station, based on a pre-built satisfaction model for charging time and charging price, determine the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time according to the vehicle arrival time and the time-of-use electricity price, wherein different user types have different sensitivities to charging time and charging price;

[0183] For each vehicle arrival time, the vehicle departure time is determined based on the vehicle arrival time. Based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined. Based on the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time, the upper and lower bounds of the charging station load power are obtained by using Minkowski summation.

[0184] Based on a pre-built comprehensive benefit model of the charging station, the configuration strategy of the charging station is solved according to the upper and lower bounds of the charging station's load power.

[0185] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0186] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0187] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0188] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0189] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0190] This disclosure also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the methods of any of the above embodiments. The execution method and beneficial effects are similar, and will not be described again here.

[0191] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0192] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring a charging station, characterized in that, include: For each vehicle arrival time within the operation cycle of a charging station, based on a pre-built satisfaction model for charging time and charging price, the most satisfactory charging time information corresponding to the target user type for the vehicle arrival time is determined according to the vehicle arrival time and the time-of-use electricity price. Among them, different user types have different sensitivities to charging time and charging price. For each vehicle arrival time, the vehicle departure time is determined based on the vehicle arrival time. Based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined. Based on the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time, the upper and lower bounds of the charging station load power are obtained by using Minkowski summation. Based on the pre-built comprehensive benefit model of the charging station, the configuration strategy of the charging station is solved according to the upper and lower bounds of the charging station load power. The comprehensive benefit model of the charging station includes constraints on charging piles within the charging station, constraints on energy storage within the charging station, constraints on the capacity of distribution transformers within the charging station, constraints on peak shaving auxiliary services of the charging station, constraints on valley filling auxiliary services of the charging station, and comprehensive costs. The pre-built comprehensive benefit model for charging stations, based on the upper and lower bounds of the charging station's load power, solves for the configuration strategy of the charging station, including: For existing battery swapping stations, based on the upper and lower limits of the charging station's load power, the capacity configuration of the distribution transformer, and the energy storage capacity configuration, the solution is to determine the configuration of the number of charging piles that minimizes the overall cost. For existing charging stations, based on the upper and lower limits of the charging station load power, the capacity configuration of the distribution transformer, and the number of charging piles, the energy storage capacity configuration that minimizes the overall cost is determined. For the charging station to be built, based on the upper and lower limits of the charging station's load power and the configuration of the number of charging piles, the corresponding distribution transformer capacity configuration and energy storage capacity configuration that minimize the overall cost are calculated.

2. The method according to claim 1, characterized in that, The method, based on a pre-built satisfaction model for charging time and charging price, determines the most satisfactory charging time information for the target user type corresponding to the vehicle's arrival time, according to the vehicle's arrival time and time-of-use electricity price. This includes: The target user type corresponding to the vehicle's arrival time is randomly selected from multiple user types; Obtain the first weight value of the target user type for charging time and the second weight value for charging price; Based on the vehicle arrival time, the time-of-use electricity price, the vehicle charging time, the first weight value, and the second weight value, the satisfaction level of the target user type under different charging periods is determined, and the most satisfactory charging time information is determined based on the charging period corresponding to the highest satisfaction level. The vehicle charging time is calculated based on the vehicle's initial SOC and the preset charging power, and the vehicle's initial SOC is obtained based on normal distribution sampling.

3. The method according to claim 2, characterized in that, The most satisfactory charging time information includes the most satisfactory start time for charging; The step of determining the satisfaction level of the target user type under different charging periods based on the vehicle arrival time, the time-of-use electricity price, the vehicle charging time, the first weight value, and the second weight value, and determining the most satisfactory charging time information based on the charging period corresponding to the highest satisfaction level, includes: For each preset extension period, the satisfaction level of the target user type during the charging period corresponding to the preset extension period is determined based on the first charging price, the second charging price, the vehicle charging time, the extension duration corresponding to the preset extension period, the first weight value, and the second weight value. The first charging price is the charging price from the moment the vehicle arrives at the station until it is fully charged; the second charging price is the charging price during the charging period corresponding to the preset extension period; and the extension duration is the product of the preset extension period and the unit duration. The preset extension period corresponding to the highest satisfaction level is taken as the target extension period. The optimal charging start time is obtained by extending the vehicle arrival time backward by the target extension period.

4. The method according to claim 3, characterized in that, The step of determining the vehicle departure time based on the vehicle arrival time, and determining the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, includes: By subtracting the vehicle's charging time backward from its departure time, the latest time the vehicle started charging can be obtained. The upper and lower bounds of the vehicle load power are obtained based on the most satisfactory start time of charging, the latest start time of charging of the vehicle, and the departure time of the vehicle corresponding to the target user type.

5. The method according to claim 2, characterized in that, The optimal charging time information includes the target charging delay; The step of determining the satisfaction level of the target user type under different charging periods based on the vehicle arrival time, the time-of-use electricity price, the vehicle charging time, the first weight value, and the second weight value, and determining the most satisfactory charging time information based on the charging period corresponding to the highest satisfaction level, includes: For each preset power reduction, the satisfaction level of the target user type during the charging period corresponding to the preset power reduction is determined based on the first charging price, the compensation electricity price corresponding to the preset power reduction, the vehicle charging time, the power reduction charging delay corresponding to the preset power reduction, the first weight value, and the second weight value. The first charging price is the charging price from the moment the vehicle arrives at the station until it is fully charged. The compensation electricity price is the electricity price compensated when the charging power is reduced by the preset power reduction. The power reduction charging delay is the extended charging time caused by the reduction in the charging power. The preset power reduction corresponding to the maximum satisfaction level is taken as the target power reduction. The power reduction charging delay corresponding to the target power reduction is taken as the target charging delay.

6. The method according to claim 5, characterized in that, The step of determining the vehicle departure time based on the vehicle arrival time, and determining the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, includes: The sum of the vehicle charging time and the target charging delay corresponding to the target user type is taken as the new vehicle charging time. By subtracting the new vehicle charging time backward from the vehicle departure time, the latest charging start time with reduced power can be obtained; The upper and lower bounds of the vehicle load power are obtained based on the vehicle's arrival time, the latest start time of the reduced power charging, and the vehicle's departure time.

7. A configuration device for a charging station, characterized in that, include: The first determining module is used to determine the most satisfactory charging time information corresponding to the target user type for each vehicle arrival time within the operation cycle of the charging station, based on a pre-built satisfaction model for charging time and charging price, and according to the vehicle arrival time and time-of-use electricity price. Different user types have different sensitivities to charging time and charging price. The second determining module is used to determine the vehicle departure time for each vehicle arrival time, and to determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time based on the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time. The upper and lower bounds of the charging station load power are obtained by using Minkowski summation based on the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time. The solution module is used to solve the configuration strategy of the charging station based on the pre-built comprehensive benefit model of the charging station and according to the upper and lower bounds of the charging station load power. The comprehensive benefit model of the charging station includes constraints on charging piles within the charging station, constraints on energy storage within the charging station, constraints on the capacity of distribution transformers within the charging station, constraints on peak shaving auxiliary services of the charging station, constraints on valley filling auxiliary services of the charging station, and comprehensive costs. The pre-built comprehensive benefit model for charging stations, based on the upper and lower bounds of the charging station's load power, solves for the configuration strategy of the charging station, including: For existing battery swapping stations, based on the upper and lower limits of the charging station's load power, the capacity configuration of the distribution transformer, and the energy storage capacity configuration, the solution is to determine the configuration of the number of charging piles that minimizes the overall cost. For existing charging stations, based on the upper and lower limits of the charging station load power, the capacity configuration of the distribution transformer, and the number of charging piles, the energy storage capacity configuration that minimizes the overall cost is determined. For the charging station to be built, based on the upper and lower limits of the charging station's load power and the configuration of the number of charging piles, the corresponding distribution transformer capacity configuration and energy storage capacity configuration that minimize the overall cost are calculated.

8. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores a computer program that, when executed by the processor, performs the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

Images