Charging station configuration method and device, equipment and storage medium

Abstract

The embodiment of the invention relates to a charging station configuration method and device, equipment and a storage medium. The method comprises the steps of determining most satisfactory charging time information corresponding to a target user type corresponding to a vehicle arrival moment according to the vehicle arrival moment and time-of-use electricity price based on a pre-constructed satisfaction model for charging time and charging price for each vehicle arrival moment in an operation period of a charging station; for each vehicle arrival moment, determining a vehicle departure moment according to the vehicle arrival moment, and determining upper and lower bounds of vehicle load power corresponding to each vehicle arrival moment according to the vehicle arrival moment, the most satisfactory charging time information corresponding to the vehicle arrival moment and the vehicle departure moment; according to the upper and lower bounds of the load power of the vehicle corresponding to the arrival time of each vehicle, utilizing Minkowski summation to obtain the upper and lower bounds of the load power of the charging station; and based on a pre-constructed charging station comprehensive benefit model, solving a configuration strategy of the charging station according to the upper and lower bounds of the load power of the charging station.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of charging stations, and in particular to a configuration method, apparatus, device, and storage medium for a charging station. Background Art

[0002] As a green and clean means of transportation and a high-quality alternative to fuel vehicles, electric vehicles are gradually becoming a new development direction for the future automotive industry. By the end of 2022, the number of electric vehicles in my country has reached 10.45 million, an increase of 63.3% compared to 2021, showing a rapid development trend.

[0003] As the number of electric vehicles increases, the demand for electric vehicle charging stations is also increasing. However, the current operation of electric vehicle charging stations still has pain points such as low overall user response and low charging pile utilization, which directly affects the economic benefits of the actual charging station operation. Therefore, how to optimize the configuration of charging stations to improve the overall comprehensive benefits has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide a configuration method, apparatus, device and storage medium for a charging station.

[0005] A first aspect of an embodiment of the present disclosure provides a method for configuring a charging station, the method comprising:

[0006] For each vehicle arrival time within the operation cycle of the charging station, 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, determine the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time, wherein different user types have different sensitivities to charging time and charging price;

[0007] For each vehicle arrival time, determine the vehicle departure time according to the vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and obtain the upper and lower bounds of the charging station load power by using Minkowski summation according to the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time;

[0008] Based on the pre-constructed 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 load power of the charging station.

[0009] A second aspect of an embodiment of the present disclosure provides a configuration device for a charging station, the device comprising:

[0010] The first determination module is used to determine the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time 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, according to the vehicle arrival time and time-of-use electricity price, wherein different user types have different sensitivities to charging time and charging price;

[0011] A second determination module is used to determine the vehicle departure time according to each vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and obtain the upper and lower bounds of the charging station load power by using Minkowski summation according to the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time;

[0012] 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 the upper and lower bounds of the load power of the charging station.

[0013] A third aspect of an embodiment of the present disclosure provides an electronic device, the server comprising: a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method of the first aspect above.

[0014] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect described above can be implemented.

[0015] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0016] The disclosed embodiments can optimize the configuration of charging stations by analyzing the user charging response characteristics (i.e., the most satisfactory charging time information corresponding to each preset user type), thereby improving the rationality of the charging station configuration, improving the utilization rate of the charging station equipment, and improving the dispatchability potential of the charging station, thereby improving the overall comprehensive benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 is a flow chart of a method for configuring a charging station provided by an embodiment of the present disclosure;

[0020] Figure 2 is a schematic diagram of upper and lower limits of vehicle load power provided by an embodiment of the present disclosure;

[0021] Figure 3 is a flow chart of another method for configuring a charging station provided by an embodiment of the present disclosure;

[0022] Figure 4 It is a structural diagram of a charging station equipment optimization configuration analysis system provided by an embodiment of the present disclosure;

[0023] Figure 5 It is a schematic diagram of input and output of each module in a charging station equipment optimization configuration analysis system provided by an embodiment of the present disclosure;

[0024] Figure 6 is a flow chart for solving the adjustable boundary of the charging station load provided by an embodiment of the present disclosure;

[0025] Figure 7 is a structural schematic diagram of a configuration device for a charging station provided in an embodiment of the present disclosure;

[0026] Figure 8 It is a structural schematic diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0029] As mentioned above, with the increase in the number of electric vehicles, the demand for electric vehicle charging stations is also increasing. However, the current operation of electric vehicle charging stations still has 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 operations. At the same time, with the continuous construction of my country's power market, charging stations or charging station clusters can participate in the peak-shaving auxiliary services of the power system as third-party entities to obtain additional benefits. However, when charging stations participate in peak-shaving auxiliary services, their regulation capabilities are affected by the charging response characteristics of electric vehicle users, and the user response characteristics are complex. In addition, electric vehicle charging loads are flexible. If the regulation capabilities of large-scale electric vehicle charging loads can be fully utilized, it will provide operators with more diversified profit and regulation methods, such as participating in the peak-shaving auxiliary service market to obtain additional benefits. However, as a large-scale electric vehicle charging load cluster, electric vehicle charging stations are currently facing 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 income of charging stations. Therefore, under the above background, how to improve the overall comprehensive benefits of charging stations through additional revenue means such as participating in peak-shaving auxiliary services and charging station configuration transformation based on the actual situation of the station is a difficult problem that needs to be solved urgently in the optimization and operation of charging stations. In terms of the response volume of electric vehicle users, researchers are currently more focused on studying the characteristics of electric vehicle users' response to electricity demand, while for the planning or optimal configuration of electric vehicle charging stations, existing research mainly studies the content of site selection and capacity determination with the goal of meeting the maximum charging demand. In the power market, the research related to the optimal configuration of charging station equipment that takes into account the charging demand of electric vehicles and the peak-shaving of the power system and the response characteristics of electric vehicle users has not become the focus of researchers. Under the above background, the embodiment of the present disclosure proposes a charging station optimization configuration method for electric vehicles participating in the peak-shaving auxiliary service market taking into account the user response characteristics. By analyzing the response characteristics of electric vehicle users, the charging station is optimized and configured to improve the rationality of the charging station configuration, thereby improving the utilization rate of the charging station equipment, and at the same time improving the dispatchable potential of the charging station, thereby improving the overall comprehensive benefits.

[0030] Figure 1 1 is a flow chart of a method for configuring a charging station provided by an embodiment of the present disclosure, and the method can be performed by an electronic device. The electronic device can be exemplarily understood as a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a smart TV, etc. Figure 1 As shown, the method provided in this embodiment includes the following steps.

[0031] 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 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.

[0032] Specifically, the operation cycle refers to the operating period of the charging station in a day.

[0033] Specifically, the vehicle arrival time is the time when the vehicle arrives at the charging station. Multiple vehicle arrival times can be evenly selected from the operation cycle, or multiple vehicle arrival times can be randomly selected from the operation cycle. This disclosure does not limit this.

[0034] Specifically, the time-of-use electricity price refers to the charging price corresponding to each moment during the operating cycle.

[0035] Specifically, the target user type may be randomly selected from multiple user types, wherein different user types have different sensitivities to charging time and charging price.

[0036] 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 of each user type should satisfy the following formula (1):

[0037] N 1 +N 2 +N 3 +N 4 +N 5 =N t Formula (1)

[0038] Table 1

[0039]

[0040] Specifically, a user satisfaction model for charging time and charging price may be constructed based on the Weber-Fechner law, but is not limited thereto.

[0041] Optionally, based on a pre-built satisfaction model for charging time and charging price, according to the vehicle arrival time and the time-of-use electricity price, the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time is determined, including:

[0042] Randomly extract the target user type corresponding to the vehicle arrival time from multiple user types;

[0043] Obtain a first weight value for the target user type with respect to the charging time and a second weight value for the charging price;

[0044] According to 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 in different charging time periods is determined, and the most satisfactory charging time information is determined according to the charging time period corresponding to the maximum satisfaction, wherein the vehicle charging time is calculated based on the vehicle's starting SOC and the preset charging power, and the vehicle's starting SOC is obtained based on normal distribution sampling.

[0045] 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 actual conditions, and are not limited here. 1 ) and the second weight value (denoted as γ 2 ), as shown in Table 2.

[0046] Table 2

[0047]

[0048] In some embodiments, the satisfaction model is a user satisfaction model that takes into account load shifting of electric vehicles, and the most satisfactory charging time information includes the most satisfactory charging start time;

[0049] Among them, according to 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 in different charging time periods is determined, and the most satisfactory charging time information is determined according to the charging time period corresponding to the maximum satisfaction, including:

[0050] For each preset number of postponed time periods, the satisfaction of the target user type in the charging time period corresponding to the preset number of postponed time periods is determined according to the first charging price, the second charging price, the vehicle charging time, the postponement time corresponding to the preset number of postponed time periods, the first weight value and the second weight value, wherein the first charging price is the charging price from the time the vehicle arrives at the station to when it is fully charged, the second charging price is the charging price of the charging time period corresponding to the preset number of postponed time periods, and the postponement time is the product of the preset number of postponed time periods and the unit time length;

[0051] The preset postponement period number corresponding to the maximum satisfaction level is used as the target postponement period number;

[0052] The vehicle arrival time is postponed by the target postponement time period to obtain the most satisfactory time to start charging.

[0053] Specifically, the preset number of postponement periods is an integer. It should be noted that the specific value range of the preset number of postponement periods and the specific duration of the unit duration can be set by those skilled in the art according to actual conditions, and are not limited here. For example, the minimum value of the preset number of postponement periods is 0 and the maximum value is n. max , the unit time length is Δt1,nmax The product of Δt1 and Δt2 can take any value in the range of 2 hours to 3 hours, but is not limited thereto.

[0054] Specifically, the first charging price is the charging price for the current charging period. The starting time of the current charging period is the time when the vehicle arrives at the station. The ending time of the current charging period is the time when charging starts from the time when the vehicle arrives at the station and is fully charged according to the preset charging power, that is, the time when the vehicle charging is postponed from the time when the vehicle arrives at the station.

[0055] Specifically, the second charging price is the charging price of the deferred charging period, and the deferred charging period is a period obtained by postponing the current charging period as a whole by the deferred time.

[0056] Specifically, the charging period corresponding to the preset deferred period number is the deferred charging period.

[0057] Specifically, according to Weber's law, the difference in the same stimulus must reach a certain ratio to cause a difference in sensation. This ratio 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 used as the unit of the sensory quantity, that is, for every increase in the difference threshold, the psychological quantity increases by one unit. The sensory quantity is proportional to the logarithm of the physical quantity, that is, the increase in the sensory quantity lags behind the increase in the physical quantity. The physical quantity increases in geometric series, while the psychological quantity increases in arithmetic series, as shown in formula (2):

[0058] S=klgR formula (2)

[0059] Among them, S is the sensation intensity, R is the stimulus intensity, and k is a constant, which is the weight value of each type of stimulus.

[0060] Based on this, the user satisfaction model considering electric vehicle load shifting includes the following formulas (3) to (6):

[0061]

[0062] maxθ formula (5)

[0063] 0≤n1≤n max Formula (6)

[0064] Among them, θ is the incentive degree of charging price and charging time to electric vehicle users, γ 1 is the first weight value, γ 2 is the second weight value, c(t) is the first charging price, c(t') is the second charging price, t ch The time it takes to charge the vehicle, SOC is the starting SOC of the vehicle, S B is the electric vehicle capacity, P ch is the charging power of the charging pile, n1 is the number of preset extension periods, and Δt1 is the unit duration.

[0065] Among them, formula (5) indicates that the solution goal is to maximize satisfaction, and formula (6) is the preset delay period number constraint, that is, it cannot be delayed indefinitely. Based on the preset delay period number with the minimum stimulation level, the user's response characteristics to immediate charging and delayed charging can be analyzed. If the target delay period number corresponding to the minimum stimulation level is 0, the user chooses immediate charging. If the target delay period number corresponding to the minimum stimulation level is not 0, the user is willing to delay charging for the target delay period number of units.

[0066] In some other embodiments, the satisfaction model is a user satisfaction model that considers the charging power control of the electric vehicle, and the most satisfactory charging time information includes the target charging delay;

[0067] Among them, according to 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 in different charging time periods is determined, and the most satisfactory charging time information is determined according to the charging time period corresponding to the maximum satisfaction, including:

[0068] For each preset power reduction, determine the satisfaction of the target user type in the charging time period corresponding to the preset power reduction according to 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, wherein the first charging price is the charging price from the time the vehicle arrives at the station to the time when it is fully charged, wherein the compensation electricity price is the electricity price compensated when the charging power is reduced by the preset power reduction, and the power reduction charging delay is the extended charging time caused by the charging power being reduced by the preset power reduction;

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

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

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

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

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

[0074]

[0075] maxθ formula (9)

[0076] 0≤ΔP≤P max Formula (10)

[0077] θ is the incentive degree of charging price and charging time to electric vehicle users, γ 1 is the first weight value, γ 2 is the second weight value, c(t) is the first charging price, Δc is the compensation price corresponding to ΔP (i.e., the user's compensation price after controlling the charging power), t ch The time it takes to charge the vehicle, SOC is the starting SOC of the vehicle, S B is the electric vehicle capacity, P c h is the charging power of the charging pile, ΔP is the preset reduced power, and t' is the charging delay (i.e., the charging time is extended due to charging power control).

[0078] S120. For each vehicle arrival time, determine the vehicle departure time according to the vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and use Minkowski summation to obtain the upper and lower bounds of the charging station load power according to the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time.

[0079] Specifically, due to the different charging prices and service fees of charging stations in different time periods, some electric vehicle users who need to charge may choose a longer charging waiting time in order to pursue a lower charging price. The charging load brought by these users is an adjustable load; another part of electric vehicle users may choose a higher charging price in order to pursue a shorter charging waiting time. The charging load brought by these users is a non-adjustable load. In order to obtain the overall adjustable boundary of the charging station load, it is necessary to analyze the adjustable load and non-adjustable load in each time period within an operation cycle.

[0080] Specifically, the vehicle departure time is the time when the vehicle arrival time is postponed by an expected time. The expected time can be set by technicians in this field according to actual conditions and is not limited here. For example, the expected time is 4 hours or 5 hours, but is not limited to this.

[0081] Specifically, the upper and lower bounds of the vehicle load power are a parallelogram, which usually refers to the limit range of charging power and discharging power set by the battery management system (BMS) during the charging process of an electric vehicle.

[0082] In some embodiments, the most satisfactory charging time information includes the most satisfactory charging start time. At this time, the vehicle departure time is determined according to the vehicle arrival time, and the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, including:

[0083] The latest charging start time of the vehicle is obtained by calculating the charging time of the vehicle backward from the time when the vehicle leaves the station;

[0084] According to the most satisfactory charging start time corresponding to the target user type, the latest charging start time of the vehicle and the time when the vehicle leaves the station, the upper and lower bounds of the vehicle load power are obtained.

[0085] For example, Figure 2 Schematic diagram of the upper and lower limits of vehicle load power provided by the embodiment of the present disclosure. Figure 2 As shown, the horizontal axis is time and the vertical axis is battery power (10 represents fully charged). The parallelogram formed by the most satisfactory charging start time, the latest charging start time of the vehicle and the time when the vehicle leaves the station is the upper and lower limits of the vehicle load power.

[0086] In other embodiments, the most satisfactory charging time information includes a target charging delay. In this case, the vehicle departure time is determined according to the vehicle arrival time, and the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time are determined according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, including:

[0087] 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;

[0088] The time required for charging the new vehicle is calculated backward from the time when the vehicle leaves the station to obtain the latest time for charging to start with reduced power.

[0089] The upper and lower bounds of the vehicle load power are obtained based on the vehicle arrival time, the latest charging time for power reduction and the vehicle departure time.

[0090] For example, Figure 2 As shown in FIG. 1 , the parallelogram formed by the vehicle arrival time, the latest charging start time for power reduction, and the vehicle departure time is the upper and lower limits of the vehicle load power.

[0091] Specifically, the Minkowski summation is used to aggregate the upper and lower bounds of the vehicle load power corresponding to the departure time of each vehicle in the operation cycle 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.

[0092] Among them, the Minkowski summation is shown in the following formula (11):

[0093] A⊕B={a+b|a∈A,b∈B}Formula (11)

[0094] The Minkowski sum of A and B is a new set (i.e. A⊕B), each element of which is the result of adding a point a in A to a point b in B.

[0095] S130. Based on the pre-built comprehensive benefit model of the charging station, and according to the upper and lower bounds of the load power of the charging station, solve the configuration strategy of the charging station.

[0096] Specifically, based on the upper and lower bounds of the charging station load power, taking into account the revenue of the ancillary service market and various constraints, a comprehensive benefit model of the charging station is constructed, and then the specific values ​​of the upper and lower bounds of the charging station load power obtained in S120 are brought into the comprehensive benefit model of the power station to solve the configuration strategy of the charging station.

[0097] For a charging station, its revenue is composed of ancillary service market revenue, user charging revenue, electricity purchase cost, energy storage cost, and charging station facility configuration cost. Its ancillary service market capacity is determined by factors such as user response characteristics, number of charging piles, energy storage capacity configuration, and distribution transformer capacity configuration. Therefore, the following three scenarios can be studied separately:

[0098] Scenario 1: For existing battery swap stations, charging piles are introduced. That is, the original distribution transformer capacity configuration and energy storage capacity configuration are known, and the number of charging piles introduced is used as the optimization variable to build a comprehensive benefit model for the charging station.

[0099] Scenario 2: Introducing energy storage to existing charging stations. That is, given the original distribution transformer capacity configuration and the number of charging piles, the energy storage capacity configuration is introduced as an optimization variable to build a comprehensive benefit model for the charging station.

[0100] Scenario 3: For the charging station to be built, the number of charging piles is known, and the capacity configuration of the distribution transformer and the energy storage capacity configuration are used as optimization variables to construct a comprehensive benefit model of the charging station.

[0101] Optionally, the comprehensive benefit model of the charging station includes constraints on charging piles in the charging station, constraints on energy storage in the charging station, constraints on the capacity of distribution transformers in the charging station, constraints on peak load shaving auxiliary services in the charging station, constraints on valley filling auxiliary services in the charging station, and comprehensive costs;

[0102] Among them, S130 includes: for the existing battery swap station, according to the upper and lower bounds of the charging station load power, the capacity configuration of the distribution transformer and the energy storage capacity configuration, solving the corresponding configuration of the number of charging piles when the comprehensive cost is minimized;

[0103] For existing charging stations, according to the upper and lower bounds of the charging station load power, the capacity configuration of the distribution transformer and the number of charging piles, the corresponding energy storage capacity configuration when the comprehensive cost is minimized is solved;

[0104] For the charging station to be built, according to the upper and lower bounds of the charging station load power and the number of charging piles, the corresponding distribution transformer capacity configuration and energy storage capacity configuration when the comprehensive cost is minimized are solved.

[0105] 1. Constraints on charging piles in charging stations

[0106]

[0107] in, and are the lower and upper bounds of the charging station load, respectively, t is the load power of the charging station after load translation, D t is the load power of the charging station before load shifting, and T is an operation cycle. Formula (12) limits the total charging power of all charging piles in the charging station to within the adjustable boundary of the charging load of the charging station (i.e., within the upper and lower limits of the charging station load power); 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.

[0108]

[0109] in, is the maximum power of the charging pile, n ch is the number of charging piles, is the maximum value that can be configured for the number of charging piles. Formula (14) limits the total charging power of all charging piles in the charging station to the maximum total charging power.

[0110]

[0111] in, The grid power of the charging station transformer, is the energy storage charging power. Formula (15) is the power balance in the charging station during time period t.

[0112] 2. Energy storage constraints in charging stations

[0113]

[0114] E 0,sto =βS sto Formula (18)

[0115]

[0116] in, is the energy storage charging power, α is the energy storage discharge rate, that is, assuming that the energy storage charging and discharging power is proportional to the capacity, α is the corresponding proportional coefficient, S sto is the energy storage capacity configuration, E t+1,sto is the amount of energy stored at time t+1, E t,sto is the energy storage capacity in time period t, is the energy storage charging efficiency, Δt is the unit time, E 0,sto is the initial energy storage capacity at the beginning of scheduling, β is the ratio of the initial energy storage capacity to the total capacity, that is, the initial capacity ratio, and are the lower and upper limits of energy storage capacity. Formula (16) is the energy storage charging power constraint, formula (17) is the energy storage capacity balance constraint, formula (18) is the energy storage initial capacity constraint, and formula (19) is the energy storage capacity safety constraint.

[0117] 3. Capacity constraints of distribution transformers in charging stations

[0118]

[0119] Among them, -S DT is the Internet access power, S DT is the grid power, is the power purchased by the charging station, and formula (20) is the power constraint for the charging station to be online and offline.

[0120] 4. Constraints on peak load reduction auxiliary services at charging stations

[0121]

[0122] Among them, P + is the target power regulation that the charging station needs to achieve in participating in the peak shaving auxiliary service market, M1 is a sufficiently large constant, and the peak shaving auxiliary service power of the charging station is is the first Boolean variable, T + It is a collection of peak shaving auxiliary service periods. Formula (21) enables the configured energy storage capacity to meet the power regulation access conditions for charging stations to participate in the ancillary service market.

[0123] 5. Constraints on charging station valley filling auxiliary services

[0124]

[0125] Among them, P - is the target power regulation that the charging station needs to achieve in the valley-filling auxiliary service market, M2 is a sufficiently large constant, and the valley-filling auxiliary service power of the charging station is is the second Boolean variable, T - To fill the valley auxiliary service period collection, The formula (24) enables the configured energy storage capacity to meet the power regulation access conditions for the charging station to participate in the auxiliary service market.

[0126] 6. Determine the objective function. The objective functions of different scenarios are consistent, which is to minimize the overall cost.

[0127] The objective function is as follows:

[0128]

[0129] W 1 =c sto S sto +c ch n ch +c t S t Formula (28)

[0130]

[0131]

[0132] Where n is the number of operating years, r is the maximum operating years, c sto 、c ch and c t are the costs of energy storage capacity configuration, charging pile quantity configuration and distribution transformer configuration, S t is the capacity of the distribution transformer, c loss is the unit price of energy storage loss cost, For energy storage charging efficiency, is the energy storage charging power, Δt is the unit time, The unit price of electricity purchased from the power grid, Purchase power for charging stations, and are the unit price of ancillary service market revenue, Cut peak power consumption for charging stations. Fill the valley power for the charging station, δ t is the electricity price for electric vehicle users, c p is the unit price of charging pile loss, The maximum charging power of the charging pile. W 1 To W 6 The purchase cost of charging piles and energy storage and other in-station equipment, energy storage loss cost, energy market electricity purchase cost, auxiliary service income for peak load shifting, electric vehicle charging income, and charging pile loss cost. W is the comprehensive cost, and the optimization goal is to minimize the comprehensive cost.

[0133] For the above three scenarios, the known configurations and unknown configurations to be solved are different in different scenarios. For scenario one, the inputs of the comprehensive benefit model of the charging station are various electricity prices, energy storage capacity configurations, distribution transformer capacity configurations, and upper and lower bounds of the charging station load power. The solution process is as follows: the first step is to set the optimization variables. The optimization variables of scenario one are the configuration of the number of charging piles; the second step is to take into account the constraints and determine the objective function. The output of the solution should be the specific value of the configuration of the number of charging piles and the final total benefit. For scenarios two and three, the solution process is the same as scenario one, but the inputs of scenario two are various electricity prices, the configuration of the number of charging piles, the configuration of the capacity of the distribution transformer, and the upper and lower bounds of the charging station load power. The optimization variables are set to the energy storage capacity configuration, and the output of the solution should be the specific value of the energy storage capacity configuration and the final total benefit. The inputs of scenario three are various electricity prices, the configuration of the number of charging piles, and the upper and lower bounds of the charging station load power. The optimization variables are set to the distribution transformer capacity configuration and the energy storage capacity configuration. The output of the solution should be the specific value of the distribution transformer capacity configuration and the energy storage capacity configuration and the final total benefit.

[0134] The disclosed embodiments can construct a satisfaction model with charging time and charging price as 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 be more in line with the psychology of electric vehicle users; and, can calculate the adjustable load and non-adjustable load of the charging station in each time period according to the charging response characteristics of users in each time period within an operation cycle, and obtain the adjustable boundary of the charging station load (i.e., the upper and lower bounds of the charging station load power), which is more in line with reality; and by constructing a comprehensive benefit model of the charging station, based on the upper and lower bounds of the charging station load power, and taking into account the solution of relevant constraints, it can better solve the actual problems of charging stations in different scenarios, thereby improving the overall comprehensive benefit of the charging station.

[0135] The following describes in detail the configuration method of the charging station provided in the embodiment of the present disclosure with reference to a specific example. Figure 3 is a flow chart of another method for configuring a charging station provided in an embodiment of the present disclosure. Figure 4 is a structural diagram of a charging station equipment optimization configuration analysis system provided by an embodiment of the present disclosure, Figure 5 is a schematic diagram of input and output of each module in a charging station equipment optimization configuration analysis system provided by an embodiment of the present disclosure, Figure 6 1 is a flow chart for solving the adjustable boundary of charging station load provided by the embodiment of the present disclosure. Figure 3-6As shown in the figure, firstly, a user satisfaction model for charging time and charging price is constructed based on the Weber-Fechner law. The charging time and time-of-use electricity price are taken as input, and the vehicle starting SOC is obtained based on normal distribution sampling. The response characteristics of electric vehicle users to instant charging and delayed charging are analyzed, and the charging decision-making behavior of electric vehicle users in different time periods at the charging station (i.e., user response characteristics) is obtained. Specifically, in the first step, users in peak, valley and normal periods are accurately classified according to their sensitivity to charging time and charging price, and different weight values ​​are assigned to the two characteristic quantities of charging time and charging price based on different user categories; in the second step, a satisfaction model for charging time and charging price is constructed based on the Weber-Fechner law; the vehicle starting SOC at the time of vehicle arrival is obtained by sampling based on the normal distribution of starting SOC, and the time required for users to fully charge (i.e., vehicle charging time) is calculated based on the vehicle starting SOC; in the third step, based on the user arrival time (i.e., vehicle arrival time), time-of-use electricity price and vehicle charging time, and the weight values ​​of the two characteristic quantities of charging time and charging price corresponding to the user type, the satisfaction of the same type of users at the same vehicle arrival time under different charging time periods and time-of-use electricity prices is calculated; based on the principle of maximum satisfaction, the number of postponed time periods for this type of users (i.e., the target number of postponed time periods) is calculated, and the most satisfactory starting charging time (i.e., the most satisfactory charging time for users) is determined based on this target number of postponed time periods. In this way, for each type of electric vehicle in each time period within an operation cycle, the number of time periods willing to be postponed can be calculated, and the number of users willing to control the charging power can also be analyzed by judging whether this number of time periods is 0. For example, Figure 6 As shown, the operation cycle includes 24 time periods, each of which is 1 hour. In each time period, N vehicles arrive at the station. For each vehicle, the user type is randomly selected, and then the most satisfactory charging time for the user of the vehicle is calculated.

[0136] Then, taking the time when the vehicle leaves the station as input, combined with the charging behavior decision of the electric vehicle users in each period of the charging station, the upper and lower bounds of the vehicle load power in each period of the charging station are calculated, so as to obtain the adjustable boundary of the charging station load (i.e., the upper and lower bounds of the charging station load power) based on the Minkowski summation. Specifically, in the first step, taking the time when the vehicle arrives at the station, the time when the vehicle leaves the station, and the most satisfactory time to start charging as input, the latest time to start charging of the vehicle is first calculated based on the time when the vehicle leaves the station and the time taken to charge the vehicle; in the second step, combining the time when the vehicle arrives at the station, the time when the vehicle leaves the station, and the latest time to start charging, the upper and lower bounds of the electric vehicle charging power load are analyzed with the most satisfactory time to start charging as the user charging start time; in the third step, the upper and lower bounds of the charging station load power are obtained using the Minkowski summation.

[0137] Finally, based on the adjustable boundary of the charging station load, considering the revenue of the ancillary service market and taking into account various constraints, a comprehensive benefit model of the charging station is constructed to obtain the overall optimal configuration strategy of the charging station under different scenarios. Specifically, the first step is to model the comprehensive benefits of charging stations based on actual conditions and divide the optimal configuration of charging stations into three scenarios: 1) For existing battery swap stations, charging equipment is introduced, that is, the original distribution transformer capacity configuration and energy storage capacity configuration are known, and the number of charging piles is introduced as the optimization variable; 2) For existing charging stations, energy storage is introduced, that is, the original distribution transformer capacity configuration and the number of charging piles are known, and the energy storage capacity configuration is introduced as the optimization variable; 3) For charging stations to be built, the number of charging piles is known, and the distribution transformer capacity configuration and energy storage capacity configuration are used as optimization variables; the second step is to use the energy market power purchase price, user charging price, charging station facility capacity configuration and charging station load characteristics as the input of the charging station comprehensive benefit model, and the values ​​of the optimization variables of each scenario and the final total benefit as the model output, and the maximum total benefit is used as the objective function, taking into account various constraints including the load adjustable boundary, and solving the above three scenario models. In this way, the optimal configuration of charging station equipment is achieved.

[0138] Figure 7 is a schematic diagram of a configuration device of a charging station provided by an embodiment of the present disclosure. The configuration device of the charging station can be understood as the above-mentioned electronic device or a part of the functional modules in the above-mentioned electronic device. Figure 7 As shown, the configuration device of the charging station includes:

[0139] The first determination module 710 is used to determine the most satisfactory charging time information corresponding to the target user type corresponding to 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, 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;

[0140] The second determination module 720 is used to determine the vehicle departure time according to each vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and obtain the upper and lower bounds of the charging station load power according to the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time by using Minkowski summation;

[0141] The solution 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 the upper and lower bounds of the load power of the charging station.

[0142] Optionally, the first determination module 710 includes a first determination submodule, which is 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, and the first determination submodule includes:

[0143] An extraction submodule, used for randomly extracting the target user type corresponding to the vehicle arrival time from multiple user types;

[0144] A first acquisition submodule, used to acquire a first weight value of the target user type for charging time and a second weight value for charging price;

[0145] The first determination submodule is used to determine the satisfaction of the target user type in different charging time periods according to the vehicle arrival time, the time-of-use electricity price, the vehicle charging time, the first weight value and the second weight value, and determine the most satisfactory charging time information according to the charging time period corresponding to the maximum satisfaction, wherein the vehicle charging time is calculated based on the vehicle starting SOC and the preset charging power, and the vehicle starting SOC is obtained based on normal distribution sampling.

[0146] Optionally, the most satisfactory charging time information includes the most satisfactory charging start time;

[0147] Among them, the first determination submodule is specifically used to determine, for each preset number of deferred time periods, the satisfaction of the target user type in the charging time period corresponding to the preset number of deferred time periods according to the first charging price, the second charging price, the time taken to charge the vehicle, the deferred time corresponding to the preset number of deferred time periods, the first weight value and the second weight value, wherein the first charging price is the charging price from the time the vehicle arrives at the station to when it is fully charged, the second charging price is the charging price for the charging time period corresponding to the preset number of deferred time periods, and the deferred time is the product of the preset number of deferred time periods and the unit time length;

[0148] The preset postponement period number corresponding to the maximum satisfaction level is used as the target postponement period number;

[0149] The vehicle arrival time is postponed backward by a delay time corresponding to the target delay time period to obtain the most satisfactory charging start time.

[0150] Optionally, the second determination module 720 includes a second determination submodule, which is used to determine the vehicle departure time according to the vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time. The second determination submodule is specifically used to reverse the vehicle charging time from the vehicle departure time to obtain the latest charging start time for the vehicle;

[0151] The upper and lower bounds of the vehicle load power are obtained according to the most satisfactory charging start time corresponding to the target user type, the latest charging start time of the vehicle and the vehicle departure time.

[0152] Optionally, the most satisfactory charging time information includes a target charging delay;

[0153] Among them, the first determination submodule is specifically used to determine the satisfaction of the target user type in the charging time period corresponding to the preset power reduction for each preset power reduction according to 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, wherein the first charging price is the charging price from the time the vehicle arrives at the station to the time when it is fully charged, wherein the compensation electricity price is the electricity price compensated when the charging power is reduced by the preset power reduction, and the power reduction charging delay is the extended charging time caused by the charging power being reduced by the preset power reduction;

[0154] The preset power reduction corresponding to the maximum satisfaction level is used as the target power reduction;

[0155] The power reduction charging delay corresponding to the target power reduction is used as the target charging delay.

[0156] Optionally, the second determination module 720 includes a second determination submodule, which is used to determine the vehicle departure time according to the vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time. The second determination submodule is specifically 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;

[0157] The new charging time of the vehicle is calculated backward from the time when the vehicle leaves the station to obtain the latest time when charging starts with reduced power;

[0158] The upper and lower bounds of the vehicle load power are obtained according to the vehicle arrival time, the latest time to start charging with reduced power, and the vehicle departure time.

[0159] Optionally, the comprehensive benefit model of the charging station includes constraints on charging piles in the charging station, constraints on energy storage in the charging station, constraints on the capacity of distribution transformers in the charging station, constraints on peak load shaving auxiliary services in the charging station, constraints on valley load filling auxiliary services in the charging station, and comprehensive costs;

[0160] The solution module 730 is specifically used to solve the configuration of the number of charging piles corresponding to the minimum comprehensive cost for the existing battery swap station according to the upper and lower bounds of the load power of the charging station, the capacity configuration of the distribution transformer and the energy storage capacity configuration;

[0161] For existing charging stations, according to the upper and lower bounds of the load power of the charging station, 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 solved;

[0162] For the charging station to be built, according to the upper and lower bounds of the load power of the charging station and the number of charging piles, the corresponding distribution transformer capacity configuration and energy storage capacity configuration when the comprehensive cost is minimized are solved.

[0163] The device provided in this embodiment can execute the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.

[0164] An embodiment of the present disclosure further provides an electronic device, which includes: a memory, in which a computer program is stored; and a processor, for executing the computer program. When the computer program is executed by the processor, the method of any of the above embodiments can be implemented.

[0165] For example, Figure 8 Schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 8 , which shows a schematic diagram of the structure of an electronic device 800 suitable for implementing the embodiment of the present disclosure. The electronic device 800 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0166] like Figure 8As shown, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processing unit, 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. In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0167] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device 800 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 8 The electronic device 800 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0168] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 809, or installed from a storage device 808, or installed from a ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0169] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0170] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0171] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0172] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: 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, according to the vehicle arrival time and time-of-use electricity price, determines the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time, wherein different user types have different sensitivities to charging time and charging price;

[0173] For each vehicle arrival time, determine the vehicle departure time according to the vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and obtain the upper and lower bounds of the charging station load power by using Minkowski summation according to the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time;

[0174] Based on the pre-constructed 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 load power of the charging station.

[0175] Computer program code for performing the operations of the present disclosure may 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, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0176] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0177] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0178] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0179] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0180] The embodiments of the present disclosure further provide a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any of the above embodiments can be implemented. The execution method and beneficial effects are similar and will not be repeated here.

[0181] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants 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, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0182] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be 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 the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to 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 the charging station, 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, determine the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time, wherein different user types have different sensitivities to charging time and charging price; For each vehicle arrival time, determine the vehicle departure time according to the vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and obtain the upper and lower bounds of the charging station load power by using Minkowski summation according to the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time; Based on the pre-constructed 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 load power of the charging station.

2. The method according to claim 1, characterized in that The method based on the pre-built satisfaction model for charging time and charging price, and determining 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, includes: Randomly extracting the target user type corresponding to the vehicle arrival time from multiple user types; Obtaining a first weight value for the target user type with respect to charging time and a second weight value for charging price; The satisfaction of the target user type in different charging time periods is determined according to the vehicle arrival time, the time-of-use electricity price, the vehicle charging time, the first weight value and the second weight value, and the most satisfactory charging time information is determined according to the charging time period corresponding to the maximum satisfaction, wherein the vehicle charging time is calculated according to the vehicle starting SOC and the preset charging power, and the vehicle starting 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 charging start time; The method of determining the satisfaction of the target user type in different charging time periods according to 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 according to the charging time period corresponding to the maximum satisfaction includes: For each preset number of postponed time periods, determine the satisfaction of the target user type in the charging time period corresponding to the preset number of postponed time periods according to the first charging price, the second charging price, the time taken to charge the vehicle, the postponed time corresponding to the preset number of postponed time periods, the first weight value, and the second weight value, wherein the first charging price is the charging price from the time the vehicle arrives at the station to when it is fully charged, the second charging price is the charging price for the charging time period corresponding to the preset number of postponed time periods, and the postponed time is the product of the preset number of postponed time periods and the unit time length; The preset postponement period number corresponding to the maximum satisfaction level is used as the target postponement period number; The vehicle arrival time is postponed backward by a delay time corresponding to the target delay time period to obtain the most satisfactory charging start time.

4. The method according to claim 3, characterized in that The determining of the vehicle departure time according to the vehicle arrival time, and determining the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, includes: The time taken for charging the vehicle is calculated backwards from the time when the vehicle leaves the station to obtain the latest time when the vehicle starts charging; The upper and lower bounds of the vehicle load power are obtained according to the most satisfactory charging start time corresponding to the target user type, the latest charging start time of the vehicle and the vehicle departure time.

5. The method according to claim 2, characterized in that: The most satisfactory charging time information includes a target charging delay; The method of determining the satisfaction of the target user type in different charging time periods according to 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 according to the charging time period corresponding to the maximum satisfaction includes: For each preset power reduction, determine the satisfaction of the target user type in the charging time period corresponding to the preset power reduction according to 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, wherein the first charging price is the charging price from the time the vehicle arrives at the station to the time when it is fully charged, wherein the compensation electricity price is the electricity price compensated when the charging power is reduced by the preset power reduction, and the power reduction charging delay is the extended charging time caused by the charging power being reduced by the preset power reduction; The preset power reduction corresponding to the maximum satisfaction level is used as the target power reduction; The power reduction charging delay corresponding to the target power reduction is used as the target charging delay.

6. The method according to claim 5, characterized in that The determining of the vehicle departure time according to the vehicle arrival time, and determining the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to 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 used as a new vehicle charging time; The new charging time of the vehicle is calculated backward from the time when the vehicle leaves the station to obtain the latest time when charging starts with reduced power; The upper and lower bounds of the vehicle load power are obtained according to the vehicle arrival time, the latest time to start charging with reduced power, and the vehicle departure time.

7. The method according to claim 1, characterized in that The comprehensive benefit model of the charging station includes constraints on charging piles in the charging station, constraints on energy storage in the charging station, constraints on the capacity of distribution transformers in the charging station, constraints on peak-shaving auxiliary services in the charging station, constraints on valley-filling auxiliary services in the charging station, and comprehensive costs; The configuration strategy of the charging station is solved based on the pre-built comprehensive benefit model of the charging station and the upper and lower bounds of the load power of the charging station, including: For existing battery swap stations, according to the upper and lower bounds of the charging station load power, the capacity configuration of the distribution transformer and the energy storage capacity configuration, the corresponding number of charging piles when the comprehensive cost is minimized is solved; For existing charging stations, according to the upper and lower bounds of the load power of the charging station, the capacity configuration of the distribution transformer and the number of charging piles, the corresponding energy storage capacity configuration when the comprehensive cost is minimized is solved; For the charging station to be built, according to the upper and lower bounds of the load power of the charging station and the number of charging piles, the corresponding distribution transformer capacity configuration and energy storage capacity configuration when the comprehensive cost is minimized are solved.

8. A configuration device for a charging station, characterized in that: include: The first determination module is used to determine the most satisfactory charging time information corresponding to the target user type corresponding to the vehicle arrival time 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, according to the vehicle arrival time and time-of-use electricity price, wherein different user types have different sensitivities to charging time and charging price; A second determination module is used to determine the vehicle departure time according to each vehicle arrival time, and determine the upper and lower bounds of the vehicle load power corresponding to each vehicle arrival time according to the vehicle arrival time, the most satisfactory charging time information corresponding to the vehicle arrival time, and the vehicle departure time, and obtain the upper and lower bounds of the charging station load power by using Minkowski summation according to 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 the upper and lower bounds of the load power of the charging station.

9. An electronic device, characterized in that: include: A processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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